Suffix-based data structures are widely used for pattern recognition. In one dimension, there exist suffix automata and suffix trees, whereas in two dimensions only suffix trees are known [1]. A two-dimensional suffix at$(i, j)$is the largest square submatrix starting there and may not be unique. We introduce the CDAIG, an automaton that recognizes such two-dimensional suffixes analogously to the Isuffix Tree [1] and is built by adapting [2]. We evaluated the complexity of the CDAIG by aligning 200 randomly selected 1000 bp-long coding chunks from the Escherichia coli genome with homologs from Enterobacteriaceae species identified by BLAST [3]. Each alignment was truncated to 750 bp, all differences (substitutions and gaps) were counted, and a CDAIG was built for each pair. Figure 1 shows that edge and suffix counts increase with the number of differences, capturing the rise in sequence divergence and reflecting the phylogenetic distance between E. coli and the compared species. Datasets are available at [E. coli dataset], and the full implementation at [CDAIG data structure].

High-throughput crystallography experiments at synchrotron facilities generate massive volumes of raw diffraction data, challenging storage and long-term data management. The Hierarchical Data Format version 5 (HDF5) is widely used to organize these datasets, but its native compressors often provide limited compression ratios. In this study, we systematically evaluate a broad range of lossless compression algorithms on rotational macromolecular crystallography (MX) and serial synchrotron crystallography (SSX) datasets, including HDF5-native coders. Our results show that preprocessing strategies significantly influence compression performance, with some codecs benefiting from bit- or Byteshuffling, while others perform optimally without it. JPEG XL and the algorithm described in [1] achieve the highest weighted average compression ratios across all datasets, outperforming the best HDF5-native methods. These findings provide a benchmark for selecting optimal lossless compression strategies for large-scale crystallographic datasets.

In recent years, neural networks have been a key advance in the field of image compression. However, although their performance is highly competitive with established conventional methods, their high computational cost limits their use in settings where available power is a significant limitation, such as satellites and other remote sensing platforms. This problem has been addressed by recent contributions that have proposed reduced-complexity neural image codecs based on compressive autoencoders with hyperprior. These codecs allow for joint compression and denoising, variable-rate or fixed-quality compression. This paper proposes a comparison of the theoretical complexity of several neural codecs, as well as their encoding runtime and power consumption on low-power devices. This study includes comparisons between fixed-rate, precise rate allocation, and fixed-quality compression modes. The viability of their deployment on remote sensing missions is discussed based on these results.

Lossy compression of multispectral data has been widely adopted to tackle the downlink bottleneck in space-based Earth observation. However, compression artifacts may interact with the instrumental noise inherent to the sensors, affecting both the performance of the compressors and the effectiveness of denoising techniques. This letter evaluates the effects of lossy compression in the presence of instrumental noise and the corresponding denoising procedures on multispectral data, comparing current techniques for on-board lossy compression (CCSDS 122.0-B-1, near-lossless CCSDS 123.0-B-2, JPEG 2000, and exogenous KLT-JPEG 2000) with recent reduced-complexity models based on neural networks (MCOS and SORTENY). The rate-distortion performance of each codec is analysed across four simulated instrumental noise conditions: thermal noise, dark signal, missing lines and parallax-induced band misalignment. Experimental results show that all the evaluated codecs are resilient under thermal noise, dark signal and missing line artifacts. However, for band misalignment artifacts, the compressors using a linear spectral transform, as E-KLT-JPEG 2000 and SORTENY, experienced a significant degradation.

As more and more multispectral and hyperspectral platforms are deployed for Earth observation (EO), limited downlink capacity increases the pressure for more efficient data compression algorithms. Machine learning (ML) has been successfully applied to produce highly competitive compression models though this performance has typically been at the cost of high computational complexity, a crucial limitation for on-board remote sensing data compression. To address these issues, a reduced-complexity multispectral and hyperspectral data compression architecture is proposed. Using separate spectral and spatial transforms, the complexity of the proposed models is scalable on the number of bands, regardless of the compression ratios. This proposal outperforms state-of-the-art ML compression models as well as established lossy compression methods such as JPEG 2000 prepended with a spectral Karhunen-Loève transform (KLT) on a variety of remote sensing data sources. The performance improvement is achieved with a lower complexity than said ML models. To reproduce our results, training and test data are publicly available athttps://gici.uab.cat/GiciWebPage/datasets.phpand source code athttps://github.com/smijares/mbhs2025.

The Consultative Committee for Space Data Systems (CCSDS) first standardized near-lossless coding capabilities in the CCSDS 123.0-B-2 algorithm. However, this standard does not describe strategies to produce high-throughput hardware implementations, which are not trivial to derive from its definition. At the same time, throughput optimizations without significant compression performance penalty are paramount to enable real-time compression on-board next-generation satellites. This work demonstrates that the weight update stage of the CCSDS 123.0-B-2 predictor can be selectively bypassed to enhance throughput for both lossless and near-lossless modes with minimal impact on compression performance and still produce fully compliant bitstreams. Skipping the weight update implies that those weights must be carefully chosen outside the original CCSDS 123.0-B-2 pipeline. Two strategies are proposed to select effective weight values based on whether a priori information about the current image is exploited or not. Comprehensive experimental results are presented for both proposed strategies and for lossless and near-lossless regimes, using a representative set of hyperspectral images. The coding penalty is, on average, 1% for lossless and 8% for near-lossless, depending on the strategy used to set the initial weights. The proposed method obtains a maximum throughput of 1 processed sample per clock cycle when it is evaluated using High-Level Synthesis (HLS), consuming 4.6% of the LUTs and 31.1% of the internal memory on a Xilinx Kintex UltraScale space-grade FPGA.

Online collaborative tools for medical diagnosis produced from digital pathology images have experimented an increase in demand in recent years. Due to the large sizes of pathology images, rate control (RC) techniques that allow an accurate control of compressed file sizes are critical to meet existing bandwidth restrictions while maximizing retrieved image quality. Recently, some RC contributions to Region of Interest (RoI) coding for pathology imaging have been presented. These encode the RoI without loss and the background with some loss, and focus on providing high RC accuracy for the background area. However, none of these RC contributions deal efficiently with arbitrary RoI shapes, which hinders the accuracy of background definition and rate control. This manuscript presents a novel coding system based on prediction with a novel RC algorithm for RoI coding that allows arbitrary RoIs shapes. Compared to other methods of the state of the art, our proposed algorithm significantly improves upon their RC accuracy, while reducing the compressed data rate for the RoI by 30%. Furthermore, it offers higher quality in the reconstructed background areas, which has been linked to better clinical performance by expert pathologists. Finally, the proposed method also allows lossless compression of both the RoI and the background, producing data volumes 14% lower than coding techniques included in DICOM, such as HEVC and JPEG-LS.

Recent advances in generative models and the availability of large-scale benchmarks have made deepfake video generation and manipulation easier. Nowadays, the number of new hyper-realistic deepfake videos used for negative purposes is dramatically increasing, thus creating the need for effective deepfake detection methods. Although many existing deepfake detection approaches, particularly CNN-based methods, show promising results, they suffer from several drawbacks. In general, poor generalization results have been obtained under unseen/new deepfake generation methods. The crucial reason for the above defect is that CNN-based methods focus on the local spatial artifacts, which are unique for every manipulation method. Therefore, it is hard to learn the general forgery traces of different manipulation methods without considering the dependencies that extend beyond the local receptive field. To address this problem, this article proposes a framework that combines Convolutional Neural Network (CNN) with Vision Transformer (ViT) to improve detection accuracy and enhance generalizability. Our method, namedHCiT, exploits the advantages of CNNs to extract meaningful local features, as well as the ViT’s self-attention mechanism to learn discriminative global contextual dependencies in a frame-level image explicitly. In this hybrid architecture, the high-level feature maps extracted from the CNN are fed into the ViT model that determines whether a specific video is fake or real. Experiments were performed on Faceforensics++, DeepFake Detection Challenge preview, Celeb datasets, and the results show that the proposed method significantly outperforms the state-of-the-art methods. In addition, the HCiT method shows a great capacity for generalization on datasets covering various techniques of deepfake generation. The source code is available at: https://github.com/KADDAR-Bachir/HCiT

One of the main limitations to the adoption of deep learning for image compression is the need to train multiple models to compress at multiple rates. In the case of onboard remote sensing data compression, another limitation is the computational cost of the neural networks. Addressing both limitations, this paper presents a new reduced-complexity architecture for multi-rate compression of remote sensing images. The proposed architecture enables compressing at a precise user-selected rate while keeping a competitive performance in lossy compression on different sets of remote sensing data. The proposed approach is amenable for onboard deployment.

In this work, lossless compression algorithms are evaluated on a variety of real, current as-tronomical images. The test dataset comprises raw (integer) and processed (floating-point) images of discrete and extensive astronomical objects, captured by spatial or terrestrial tele-scopes. Compression techniques herein analyzed are chosen to be representative of the most recent algorithms devised for astronomical data, as well as the most commonly employed compressors employed in real observatories. Experimental results suggest that coding techniques such as RICE and HCOMPRESS, typically employed in world-class observatories such as Roque de los Muchachos, do not produce the best possible lossless compression results. Instead, JPEG-LS, LZMA and NDZIP yield the best compression ratio results for 16-bit data (2.72), floating-point data (2.38) and radio data (1.81), respectively. Therefore, the efficiency with which data are stored and transmitted by these observatories could be significantly improved by selectively employing the aforementioned algorithms.

Hyperspectral images are typically highly correlated along their spectrum, and this similarity is usually found to cluster in intervals of consecutive bands. We identified 5 such intervals in AVIRIS uncalibrated data (i.e., as captured on-board). These 5 intervals maximised the average spectral correlation along the 224 band spectrum. The resulting in-tervals were composed of bands 1–40, 41–96, 97–155, 156–165, and 166–224, as seen in the figure to the right.

This letter proposes methods to improve data size and access time for$k^{2}$-raster, a losslessly compressed data structure that provides efficient storage and real-time processing. Hyperspectral scenes from real missions are used as our testing data. In previous studies, with$k^{2}$-raster, the size of the hyperspectral data was reduced by up to 52% compared with the uncompressed data. In this letter, we continue to explore novel ways of further reducing the data size and access time. First, we examine the possibility of using the raster matrix of hyperspectral data without any padding (unpadded matrix) while still being able to compress the structure and access the data. Second, we examine some integer encoders, more specifically the Simple family. We discuss their ability to provide random element access and compare them with directly addressable codes (DACs), the integer encoder used in the original description for$k^{2}$-raster. Experiments show that the use of unpadded matrices has improved the storage size up to 6% while the use of a different integer encoder reduces the storage size up to 6% and element access time up to 20%.

On-board multi- and hyperspectral instruments acquire large volumes of data that need to be processed with the limited computational and storage resources. In this context, the CCSDS 123.0-B-2 standard emerges as an interesting option to compress multi- and hyperspectral images on-board satellites, supporting both lossless and near-lossless compression with low complexity and reduced power consumption. Nonetheless, the inclusion of a feedback loop in the CCSDS 123.0-B-2 predictor to support near-lossless compression introduces significant data dependencies that hinder real-time processing, particularly due to the presence of a quantization stage within this loop. This work provides an analysis of the aforementioned data dependencies and proposes two strategies aiming at maximizing throughput in hardware implementations and thus enabling real-time processing. In particular, through an elaborate mathematical derivation, the quantization stage is removed completely from the feedback loop. This reduces the critical path, which allows for shorter initiation intervals in a pipelined hardware implementation and higher throughput. This is achieved without any impact in the compression performance, which is identical to the one obtained by the original data flow of the predictor.

The increase in popularity of point-cloud-oriented applications has triggered the development of specialized compression algorithms. In this paper, a novel algorithm is developed for the lossless geometry compression of voxelized point clouds following an intra-frame design. The encoded voxels are arranged into runs and are encoded through a single-pass application directly on the voxel domain. This is done without representing the point cloud via an octree nor rendering the voxel space through an occupancy matrix, therefore decreasing the memory requirements of the method. Each run is compressed using a context-adaptive arithmetic encoder yielding state-of-the-art compression results, with gains of up to 15% over TMC13, MPEG's standard for point cloud geometry compression. Several proposed contributions accelerate the calculations of each run's probability limits prior to arithmetic encoding. As a result, the encoder attains a low computational complexity described by a linear relation to the number of occupied voxels leading to an average speedup of 1.8 over TMC13 in encoding speeds. Various experiments are conducted assessing the proposed algorithm's state-of-the-art performance in terms of compression ratio and encoding speeds.

Methods based on machine learning (ML) have been recently proposed to improve upon traditional block-based intra-prediction algorithms in modern video codecs [1,2]. Their performance, however, depends on the amount, quality and relevance of the training data. Furthermore, they require signaling the learned parameters to the decoder, thus increasing compressed data volumes. In this work, six new prediction modes based on fully-connected neural networks (FC-NNs) are proposed that avoid the two aforementioned shortcomings. To do so, 1-layer FC-NNs are used, whose parameters are rened by overfitting on the data samples being predicted. This allows to replicate the parameter optimization process at the decoder under a lossless compression regime without requiring any additional side information. Each proposed ML-based mode is based on a 1-layer FC-NN that predicts a block of size k k in a column-wise or row-wise manner using as input a subset of the reference samples used by traditional intra-prediction. Each subset, which varies for each column or row, is computed by averaging a number of reference samples to reduce noise [3]. Experimental results based on several video frames indicate that the proposed ML-based modes are selected as the best modes between 11% and 93% of the times. (see Table 1). When used in a hybrid intra-prediction framework that also includes HEVC's modes, the proposed ML-based modes increase prediction accuracy by between 0.56 dB and 7.01 dB PSNR, with respect to using only HEVC's modes.

Point cloud data have been put under the spotlight by many applications that play an increasingly important role in our every day lives. Their large size and ever-growing prevalent use cases have raised the interest in specialized compression algorithms for point cloud data. In this paper we propose a lossless intra-frame encoder for point cloud geometry. It relies on a single projection of the entire point cloud on a predetermined plane, combined with a context-adaptive binary arithmetic encoder. Our approach simplifies the current best performing approach for intra-frame compression. The experimental results indicate that our proposal not only improves the performance of all other intra-frame approaches, but it even surpasses the performance of state-of-the-art inter-frame approaches. Furthermore, we suggest to replace the adaptive encoder with a semi-adaptive approach for further performance gains.

Biorthogonal graph wavelet filterbanks, also known as GraphBior, are one of the most popular graph transforms used in image compression, but up to now, they could be designed based on two known admissible fundamental matrices: i) the random walk Laplacian, which heavily penalizes low degree pixels, and ii) the normalized Laplacian, which lacks a zero-DC response. By exploiting a new extension of the admissibility condition in GraphBior we propose a new fundamental matrix with the goal of distributing the errors of GraphBior more uniformly across pixels with different node degrees. Furthermore the proposed matrix preserves high energy compaction linked to the zero-DC GraphBior variation.

Nowadays entropy encoders are part of almost all data compression methods, with the Asymmetrical Numeral Systems (ANS) family of entropy encoders having recently risen in popularity. Entropy encoders based on thetabledvariant of ANS are known to provide varying performances depending on their internal design. In this paper, we present a method that calculates encoder redundancies in almost linear time, which translates in practice to thousand-fold speedups in redundancy calculations for small automatons, and allows redundancy calculations for automatons with tens of millions of states that would be otherwise prohibitive. We also address the problem of improving tabled ANS encoder designs, by employing the aforementioned redundancy calculation method in conjunction with a stochastic hill climbing strategy. The proposed approach consistently outperforms state-of-the-art methods in tabled ANS encoder design. For automatons of twice the alphabet size, experimental results show redundancy reductions around 10% over the default initialization method and over 30% for random initialization.

Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it affects later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from several instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios (CRs). In some scenarios, accurate segmentation performance can be achieved even for high CRs.

Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization error. Our near-lossless RWA (NLRWA) proposal can be followed by any entropy coding technique. Here, the NLRWA is coupled with a bitplane-based coder that supports progressive decoding. This successfully enables gradual quality refinement and lossless and near-lossless recovery. A smart strategy for selecting the NLRWA quantization steps is also included. Experimental results show that the proposed scheme outperforms the state-of-the-art lossless and the near-lossless compression methods in terms of compression ratios and quality retrieval.

Marlin [1, 2] is a Variable-to-Fixed (VF) codec optimized for high decoding speed through the use of small sized dictionaries that fit in the L1 cache of most CPUs. While the size of Marlin dictionaries is adequate for decoding, they are still too large to be encoded fast. We address this problem by proposing two techniques to reduce the alphabet size. The first technique is to encode rare symbols in their own segment, and the second is to combine Marlin dictionaries with Rice encoding, hence our name Rice-Marlin for our new codec. Using those techniques [3], we are able to reduce the size of Marlin dictionaries by a factor of 16, not only enabling faster encoding speed, but also achieving better compression efficiency.

In this paper, we analyze the effect of spatial and spectral compression on the performance of statistically based retrieval. Although the quality of the information is not completely preserved during the coding process, experiments reveal that a certain amount of compression may yield a positive impact on the accuracy of retrievals. We unveil two strategies, both with interesting benefits: either to apply a very high compression, which still maintains the same retrieval performance as that obtained for uncompressed data; or to apply a moderate to high compression, which improves the performance. As a second contribution of this paper, we focus on the origins of these benefits. On the one hand, we show that a certain amount of noise is removed during the compression stage, which benefits the retrievals performance. On the other hand, we analyze the effect of compression on spectral/spatial regularization (smoothing). We quantify the amount of information shared among the spatial neighbors for the different methods and compression ratios. We also propose a simple strategy to specifically exploit spectral and spatial relations and find that, when these relations are taken into account beforehand, the benefits of compression are reduced. These experiments suggest that compression can be understood as an indirect way to regularize the data and exploit spatial neighbors information, which improves the performance of pixelwise statistics-based retrieval algorithms.

The use of whole-slide images (WSIs) in pathology entails stringent storage and transmission requirements because of their huge dimensions. Therefore, image compression is an essential tool to enable efficient access to these data. In particular, color transforms are needed to exploit the very high degree of inter-component correlation and obtain competitive compression performance. Even though the state-of-the-art color transforms remove some redundancy, they disregard important details of the compression algorithm applied after the transform. Therefore, their coding performance is not optimal. We propose an optimization method called mosaic optimization for designing irreversible and reversible color transforms simultaneously optimized for any given WSI and the subsequent compression algorithm. Mosaic optimization is designed to attain reasonable computational complexity and enable continuous scanner operation. Exhaustive experimental results indicate that, for JPEG 2000 at identical compression ratios, the optimized transforms yield images more similar to the original than the other state-of-the-art transforms. Specifically, irreversible optimized transforms outperform the Karhunen-Loève Transform in terms of PSNR (up to 1.1 dB), the HDR-VDP-2 visual distortion metric (up to 3.8 dB), and the accuracy of computer-aided nuclei detection tasks (F1 score up to 0.04 higher). In addition, reversible optimized transforms achieve PSNR, HDR-VDP-2, and nuclei detection accuracy gains of up to 0.9 dB, 7.1 dB, and 0.025, respectively, when compared with the reversible color transform in lossy-to-lossless compression regimes.

Marlin [1] is a Variable-to-Fixed (VF) codec optimized for decoding speed. To achieve its speed, Marlin does not encode the current state of the input source, penalyzing compression ratio. In this paper we address this penalty by partially encoding the current state of the input in the lower bits of the codeword. Those bits select which chapter in the dictionary must be used to decode the next codeword. Each chapter is specialized for a subset of states, improving compression ratio. At the same time, we use one victim chapter to encode all rare symbols, increasing the efficiency of the rest of them. The decoding algorithm remains the same, only now codewords have overlapping bits. Mapping techniques allow us to combine common chapters and thus keep an efficient use of the L1 cache. We evaluate our approach with both synthetic and real data sets, and show significant improvements in low entropy sources, where compression efficiency can improve from 93.9% to 98.6%.

Spectral redundancy is a key element to be exploited in compression of remote sensing data. Combined with an entropy encoder, it can achieve competitive lossless coding performance. One of the latest techniques to decorrelate the spectral signal is the regression wavelet analysis (RWA). RWA applies a wavelet transform in the spectral domain and estimates the detail coefficients through the approximation coefficients using linear regression. RWA was originally coupled with JPEG 2000. This letter introduces a novel coding approach, where RWA is coupled with the predictor of CCSDS-123.0-B-1 standard and a lightweight contextual arithmetic coder. In addition, we also propose a smart strategy to select the number of RWA decomposition levels that maximize the coding performance. Experimental results indicate that, on average, the obtained coding gains vary between 0.1 and 1.35 bits-per-pixel-per-component compared with the other state-of-the-art coding techniques.

Digital cameras have become ubiquitous for amateur and professional applications. The raw images captured by digital sensors typically take the form of color filter array (CFA) mosaic images which must be “developed” (via digital signal processing) before they can be viewed. Photographers and scientists often repeat the “development process” using different parameters to obtain images suitable for different purposes. Since the development process is generally not invertible it is commonly desirable to store the raw (or undeveloped) mosaic images indefinitely. Uncompressed mosaic image file sizes can be more than 30 times larger than those of developed images stored in JPEG format. Thus data compression is of interest. Several compression methods for mosaic images have been proposed in the literature. However they all require a custom decompressor followed by development-specific software to generate a displayable image. In this paper a novel compression pipeline that removes these requirements is proposed. Specifically mosaic images can be losslessly recovered from the resulting compressed files and more significantly images can be directly viewed (decompressed and developed) using only a JPEG 2000 compliant image viewer. Experiments reveal that the proposed pipeline attains excellent visual quality while providing compression performance competitive to that of state-of-the-art compression algorithms for mosaic images.

Fast and efficient coding techniques are being increasingly required to meet the complexity restrictions of on-board satellite compression. The recently proposed Regression Wavelet Analysis (RWA) has proven to be highly effective as a spectral transform for coding remote sensing images. The algorithm is based on a pyramidal prediction, using multiple regression analysis, to tackle residual data dependencies in the wavelet domain. RWA combines low complexity and reversibility and has demonstrated competitive performance for lossless and progressive lossy-to-lossless compression superior to the state-of-the-art predictive-based CCSDS-123.0 and the widely used transform-based principal component analysis (PCA). In this paper we introduce a very low-complexity RWA approach, where prediction is based on only a few components, while the performance is maintained. When RWA computational complexity is taken to an extremely low level, careful model selection is necessary. Contrary to expensive selection procedures, we propose a simple and efficient strategy called neighbor selection for using small regression models. On a set of well-known and representative hyperspectral images, these small models maintain the excellent coding performance of RWA, while reducing the computational cost by about 90%.

We present Marlin, a variable-to-fixed (VF) codec optimized for decoding speed. Marlin builds upon a novel way of constructing VF dictionaries that maximizes efficiency for a given dictionary size. On a lossless image coding experiment, Marlin achieves a compression ratio of 1.94 at 2494MiB/s. Marlin is as fast as state-of-the-art high-throughput codecs (e.g., Snappy, 1.24 at 2643MiB/s), and its compression ratio is close to the best entropy codecs (e.g., FiniteStateEntropy, 2.06 at 523MiB/s). Therefore, Marlin enables efficient and high throughput encoding for memoryless sources, which was not possible until now.

Hyperspectral imaging captures the spectral responses of different wavelengths per pixel for an entire image. Because the number of spectral bands is large, efficient compression of hyperspectral images is important. Leveraging on recent advances in graph signal processing (GSP), in this paper we propose to encode a hyperspectral image in groups of ω spectral bands using graph wavelets, exploiting correlations along both the spatial and the spectral dimensions. Specifically, along the spatial dimension, we estimate the inter-pixel correlations for all adjacent pixel pairs from the last image in the previous coded band group. Along the spectral dimension, we first divide an image into different spatial regions with similar spectral responses, and encode the spectral signature (correlations along the spectrum) for each region as side information (SI). The spatial / spectral correlations are used to compute edge weights to construct a graph for signal-adaptive graph wavelet based compression. Experimental results suggest that our proposal can outperform existing schemes noticeably at comparable complexity.

Inpainting techniques based on partial differential equations (PDEs), such as diffusion processes, are gaining growing importance as a novel family of image compression methods. Nevertheless, the application of inpainting in the field of hyperspectral imagery has been mainly focused on filling in missing information or dead pixels due to sensor failures. In this letter, we propose a novel PDE-based inpainting algorithm to compress hyperspectral images. The method inpaints separately the known data in the spatial and spectral dimensions. Then, it applies a prediction model to the final inpainting solution to obtain a representation much closer to the original image. Experimental results over a set of hyperspectral images indicate that the proposed algorithm can perform better than a recent proposed extension to prediction-based standard CCSDS-123.0 at low bit rate, better than JPEG 2000 Part 2 with the DWT 9/7 as a spectral transform at all bit rates, and competitive to JPEG 2000 with principal component analysis, the optimal spectral decorrelation transform for Gaussian sources.

The Consultative Committee for Space Data Systems (CCSDS) has issued several data compression standards devised to reduce the amount of data transmitted from satellites to ground stations. This paper introduces a contextual arithmetic encoder for on-board data compression. The proposed arithmetic encoder checks the causal adjacent neighbors, at most, to form the context and uses only bitwise operations to estimate the related probabilities. As a result, the encoder consumes few computational resources, making it suitable for on-board operation. Our coding approach is based on the prediction and mapping stages of CCSDS-123 lossless compression standard, an optional quantizer stage to yield lossless or near-lossless compression and our proposed arithmetic encoder. For both lossless and near-lossless compression, the achieved coding performance is superior to that of CCSDS-123, M-CALIC, and JPEG-LS. Taking into account only the entropy encoders, fixed-length codeword is slightly better than MQ and interleaved entropy coding.

The infrared atmospheric sounding interferometer (IASI) is flying on board of the Metop satellite series, which is part of the EUMETSAT Polar System. Products obtained from IASI data represent a significant improvement in the accuracy and quality of the measurements used for meteorological models. Notably, the IASI collects rich spectral information to derive temperature and moisture profiles, among other relevant trace gases, essential for atmospheric forecasts and for the understanding of weather. Here, we investigate the impact of near-lossless and lossy compression on IASI L1C data when statistical retrieval algorithms are later applied. We search for those compression ratios that yield a positive impact on the accuracy of the statistical retrievals. The compression techniques help reduce certain amount of noise on the original data and, at the same time, incorporate spatial-spectral feature relations in an indirect way without increasing the computational complexity. We observed that compressing images, at relatively low bit rates, improves results in predicting temperature and dew point temperature, and we advocate that some amount of compression prior to model inversion is beneficial. This research can benefit the development of current and upcoming retrieval chains in infrared sounding and hyperspectral sensors.

Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet-transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain. For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.

The coding and transmission of the massive datasets captured by Earth Observation (EO) satellites is a critical issue in current missions. The conventional approach is to use compression on board the satellite to reduce the size of the captured images. This strategy exploits spatial and/or spectral redundancy to achieve compression. Another type of redundancy found in such data is the temporal redundancy between images of the same area that are captured at different instants of time. This type of redundancy is commonly not exploited because the required data and computing power are not available on board the satellite. This paper introduces a coding scheme for EO satellites able to exploit this redundancy. Contrary to traditional approaches, the proposed scheme employs both the downlink and the uplink of the satellite. Its main insight is to compute and code the temporal redundancy on the ground and transmit it to the satellite via the uplink. The satellite then uses this information to compress more efficiently the captured image. Experimental results for Landsat 8 images indicate that the proposed dual link image coding scheme can achieve higher coding performance than traditional systems for both lossless and lossy regimes.

Whole-slide images (WSIs) are high-resolution, 2D, color digital images that are becoming valuable tools for pathologists in clinical, research and formative scenarios. However, their massive size is hindering their widespread adoption. Even though lossy compression can effectively reduce compressed file sizes without affecting subsequent diagnoses, no lossy coding scheme tailored for WSIs has been described in the literature. In this paper, a novel strategy called OptimizeMCT is proposed to increase the lossy coding performance for this type of images. In particular, an optimization method is designed to find image-specific multi-component transforms (MCTs) that exploit the high inter-component correlation present in WSIs. Experimental evidence indicates that the transforms yielded by OptimizeMCT consistently attain better coding performance than the Karhunen-Loève Transform (KLT) for all tested lymphatic, pancreatic and renal WSIs. More specifically, images reconstructed at the same bitrate exhibit average PSNR values 2.85~dB higher for OptimizeMCT than for the KLT, with differences of up to 5.17 dB.

The lossless compression of Whole Slide pathology Images (WSIs) using HEVC is investigated in this paper. Recently proposed intra-prediction algorithms based on differential pulse-code modulation (DPCM) and edge prediction provide significant bitrate improvements for a wide range of natural and screen content sequences, including WSIs. However, coding times remain relatively high due to the high number (35) of modes to be tested. In this paper, FastIn-tra, a novel method that requires testing only four modes is proposed. Among these four modes, FastIntra introduces a novel median edge predictor designed to accurately predict edges in different directionalities. Performance evaluations on various WSIs show average compression time reductions of 23.5% with important lossless coding improvements as compared to current block-wise intra-prediction and DPCM-based methods.

Lossy compression techniques based on multi-component transformation (MCT) can effectively enhance the storage and transmission of whole-slide images (WSIs) without adversely affecting subsequent diagnosis processes. Component transforms that are designed for other types of images or that do not take into account all aspects of the compression algorithm applied on the transformed components yield suboptimal coding performance. Recently, an MCT optimization framework adapted to the particularities of the input WSI and the following compression was proposed, yielding superior coding performance than the state of the art. However, its time complexity is too high for practical purposes. In this work FastOptimizeMCT, a fast version of this framework based on smart sampling of regions depicting tissue, is proposed. Exhaustive experimental evidence indicates that FastOptimizeMCT exhibits reasonable time complexity results-similar to that of scanning the WSIs- and coding performance that outperforms the KLT and the OST by 1.47 dB and 1.07 dB, respectively.

The Karhunen-Loeve Transform (KLT) is a popular transform used in multiple image processing scenarios. Sometimes, the application of the KLT is not carried out as a single transform over an entire image. Rather, the image is divided into smaller spatial regions (segments), each of which is transformed by a smaller dimensional KLT. Such a situation may penalize the transform efficiency. An improvement for the segmented KLT, aiming at mitigating discontinuities arising on the edge of adjacent regions, is proposed in this paper. In the case of moderately varying image regions, discontinuities occur as the consequence of disregarded similarity between transform domains, as the order and sign of eigenvectors in the transform matrices are mismatched. In the proposed method, the KLT is adjusted to guarantee the best achievable similarity via the optimal assignment and sign correspondence for eigenvectors. Experimental results indicate that the proposed transform improves the similarity between transform domains, and reduces RMSE on the edge of adjacent regions. In consequence, images processed by the adjusted KLT present better cohesion and continuity between independently transformed regions.

The recently proposed Regression Wavelet Analysis (RWA) scheme holds a great promise as a spectral transform for compressing hyperspectral images due to its low complexity, reversibility, and demonstrated superior coding performance. The scheme is based on a pyramidal prediction, using multiple regression analysis, to exploit statistical dependence in the wavelet domain. For lossless coding, RWA has proven to offer better performance than other spectral transforms like reversible PCA and also better than the best and most recent lossless coding standard in remote sensing, CCSDS-123.0. For progressive lossy-to-lossless coding, RWA also yields improved performance as compared to PCA. In this paper, we show that the RWA parameters are unbiased for lossy coding, where the regression models are used not with the original transformed components, but with the recovered ones, which lack some information due to the lossy reconstruction. As a byproduct, we also report that the Exogenous RWA model, a variant of RWA where the employed regression parameters have been trained using other images from the same sensor, still provides almost identical performance to that obtained by applying regression on the same image, thus showing that hyperspectral images with large sizes in the spectral dimension can be coded via RWA without side information and at a lower computational cost.

The analysis techniques applied to DNA microarray images are under active development. As new techniques become available, it will be useful to apply them to existing microarray images to obtain more accurate results. The compression of these images can be a useful tool to alleviate the costs associated to their storage and transmission. The recently proposed Relative Quantizer (RQ) coder provides the most competitive lossy compression ratios while introducing only acceptable changes in the images. However, images compressed with the RQ coder can only be reconstructed with a limited quality, determined before compression. In this work, a progressive lossy-to-lossless scheme is presented to solve this problem. First, the regular structure of the RQ intervals is exploited to define a lossy-to-lossless coding algorithm called the Progressive RQ (PRQ) coder. Second, an enhanced version that prioritizes a region of interest, called the PRQ-region of interest (ROI) coder, is described. Experiments indicate that the PRQ coder offers progressivity with lossless and lossy coding performance almost identical to the best techniques in the literature, none of which is progressive. In turn, the PRQ-ROI exhibits very similar lossless coding results with better rate-distortion performance than both the RQ and PRQ coders.

A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed regression wavelet analysis (RWA) uses multivariate regression to exploit the relationships among wavelet-transformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain, thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability, and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion, and Infrared Atmospheric Sounding Interferometer, suggest that RWA outperforms not only principal component analysis and wavelets but also the best and most recent coding standard in remote sensing, CCSDS-123.

The lossless intra-prediction coding modality of the High Efficiency Video Coding standard provides high coding performance while allowing frame-by-frame basis access to the coded data. This is of interest in many professional applications, such as medical imaging, automotive vision, and digital preservation in libraries and archives. Various improvements to lossless intra-prediction coding have been proposed recently, most of them based on sample-wise prediction using differential pulse code modulation (DPCM). Other recent proposals aim at further reducing the energy of intra-predicted residual blocks. However, the energy reduction achieved is frequently minimal due to the difficulty of correctly predicting the sign and magnitude of residual values. In this paper, we pursue a novel approach to this energy-reduction problem using piecewise mapping (pwm) functions. In particular, we analyze the range of values in residual blocks and apply accordingly a pwm function to map specific residual values to unique lower values. We encode the appropriate parameters associated with the pwm functions at the encoder, so that the corresponding inverse pwm functions at the decoder can map values back to the same residual values. These residual values are then used to reconstruct the original signal. This mapping is, therefore, reversible and introduces no losses. We evaluate the pwm functions on 4 × 4 residual blocks computed after DPCM-based prediction for lossless coding of a variety of camera-captured and screen content sequences. Evaluation results show that the pwm functions can attain the maximum bitrate reductions of 5.54% and 28.33% for screen content material compared with DPCM-based and block-wise intra-prediction, respectively. Compared with intra-block copy, piecewise mapping can attain the maximum bit-rate reductions of 11.48% for a camera-captured material.

DNA microarrays are one of the fastest-growing new technologies in the field of genetic research, and DNA microarray images continue to grow in number and size. Since analysis techniques are under active and ongoing development, storage, transmission and sharing of DNA microarray images need be addressed, with compression playing a significant role. However, existing lossless coding algorithms yield only limited compression performance (compression ratios below 2:1), whereas lossy coding methods may introduce unacceptable distortions in the analysis process. This work introduces a novel Relative Quantizer (RQ), which employs non-uniform quantization intervals designed for improved compression while bounding the impact on the DNA microarray analysis. This quantizer constrains the maximum relative error introduced into quantized imagery, devoting higher precision to pixels critical to the analysis process. For suitable parameter choices, the resulting variations in the DNA microarray analysis are less than half of those inherent to the experimental variability. Experimental results reveal that appropriate analysis can still be performed for average compression ratios exceeding 4.5:1.

This letter proposes a near-lossless coder for hyperspectral images. The coding technique is fully embedded and minimizes the distortion in the l2-norm initially and in the $l_\infty$-norm subsequently. Based on a two-stage near-lossless compression scheme, it includes a lossy and a near-lossless layer. The novelties are the observation of the convergence of the entropy of the residuals in the original domain and in the spectral-spatial transformed domain and an embedded near-lossless layer. These contributions enable a progressive transmission while optimizing both signal-to-noise ratio (SNR) and peak absolute error (PAE) performance. The embeddedness is accomplished by bit-plane encoding plus arithmetic encoding. Experimental results suggest that the proposed method yields a highly competitive coding performance for hyperspectral images, outperforming multicomponent JPEG2000 for the l∞-norm and pairing its performance for the l2-norm, and also outperforming M-CALIC in the near-lossless case, i.e., for PAE ≥ 5.

It has been shown that image compression based on principal component analysis (PCA) provides good compression efficiency for hyperspectral images. However, PCA might fail to capture all the discriminant information of hyperspectral images, since features that are important for classification tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyperspectral images with pre-encoding discriminant information. A feature extraction method is first applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones. Both feature images and residual images are compressed and transmitted. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor indicate that the proposed method provides better compression efficiency with improved classification accuracy than conventional compression methods.

Spectral transforms are tools commonly employed in multi- and hyperspectral data compression to decorrelate images in the spectral domain. The pairwise orthogonal transform (POT) is one such transform that has been specifically devised for resource-constrained contexts similar to those found on board satellites or airborne sensors. Combining the POT with a 2-D coder yields an efficient compressor for multi- and hyperspectral data. However, a drawback of the original POT is that its dynamic range expansion, i.e., the increase in bit depth of transformed images, is not constant, which may cause problems with hardware implementations. Additionally, the dynamic range expansion is often too large to be compatible with the current 2-D standard CCSDS 122.0-B-1. This paper introduces the isorange POT, a derived transform that has a small and limited dynamic range expansion, compatible with CCSDS 122.0-B-1 in almost all scenarios. Experimental results suggest that the proposed transform achieves lossy coding performance close to that of the original transform. For lossless coding, the original POT and the proposed isorange POT achieve virtually the same performance.

Wavelet-based coding systems commonly employ uniform scalar deadzone quantization (USDQ) together with a bitplane coding strategy to progressively refine image quality. Our previous work presents a quantization scheme that employs 2 step sizes depending on the magnitude of the coefficients. This 2-step scalar deadzone quantization (2SDQ) scheme is introduced in the framework of JPEG2000 by modifying all coefficients within a codeblock to enhance the quality of the image while transmitting fewer bitplanes than those needed with a conventional USDQ scheme. This paper extends our prior work by applying the 2SDQ in a subblock level, i.e., in small sets of coefficients, called cells, selected within a codeblock. Combined with rate-distortion optimization techniques, the proposed cell-based 2SDQ can help to code high quality images employing even fewer bitplanes than those needed with our previous strategy. This may be especially useful for high-dynamic range images or for devices with constrained resources.

This work defines an entropy-based measure aimed to establish the compression limits of wavelet-based image coding. This measure serves to appraise the efficiency of current codecs, determining whether there is margin for their improvement or not. Also, it may help to design new compression schemes that target a particular type of images and/or compression rates.

This works focuses on the High Efficiency Video Coding (HEVC) standard as a compression method to be potentially adopted by the Digital Imaging and Communications in Medicine (DICOM) standard. We are particularly interested in improving the lossless compression efficiency of the intra coding process for grayscale anatomical medical images. We focus on intra coding due to its low complexity and outstanding compression results, as well as the fact that it allows coding high-dimensional medical images on a slice-by-slice basis. This is especially advantageous for cases when only a small set of slices needs to be accessed without the need to decode the entire data set. Based on the characteristics of grayscale anatomical medical images, specifically their large amount of edge information and frequent number of patterns depicted on various directions, we propose improvements to HEVC intra coding based on sample-by-sample (SbS) differential pulse code modulation (DPCM) with equal displacements so the density of prediction modes is constant in all directions. Performance evaluations over MRI, CT and X-ray angiography sequences show that the proposed improvements outperform current HEVC lossless intra coding, achieving average coding gains of 6%.

The transform in image coding aims to remove redundancy among data coefficients so that they can be independently coded, and to capture most of the image information in few coefficients. While the second goal ensures that discarding coefficients will not lead to large errors, the first goal ensures that simple (point-wise) coding schemes can be applied to the retained coefficients with optimal results. Principal Component Analysis (PCA) provides the best independence and data compaction for Gaussian sources. Yet, non-linear generalizations of PCA may provide better performance for more realistic non-Gaussian sources. Principal Polynomial Analysis (PPA) generalizes PCA by removing the non-linear relations among components using regression, and was analytically proved to perform better than PCA in dimensionality reduction. We explore here the suitability of reversible PPA for lossless compression of hyperspectral images. We found that reversible PPA performs worse than PCA due to the high impact of the rounding operation errors and to the amount of side information. We then propose two generalizations: Backwards PPA, where polynomial estimations are performed in reverse order, and Double-Sided PPA, where more than a single dimension is used in the predictions. Both yield better coding performance than canonical PPA and are comparable to PCA.

Context modeling is key in wavelet-based image coding schemes to achieve competitive coding performance. Commonly, context models are devised for a particular coding system and are employed for many different types of images. The aim of this work is to evaluate the suitability of three well-known context models for coding hyperspectral images, without focusing on a particular wavelet-based coding system. To do so, an entropy-based measure defined using the mechanisms utilized by modern image codecs is employed. The experimental results assess the appropriateness of the context models considering different coding rates and transform strategies. They reveal that some widely-used context models may not be as adequate as it is generally thought. The hints provided by this analysis may help to design simpler and more efficient wavelet-based codecs for hyperspectral images.

DNA micro arrays are state-of-the-art tools in biological and medical research. In this work, we discuss the suitability of lossy compression for DNA micro array images and highlight the necessity for a distortion metric to assess the loss of relevant information. We also propose one possible metric that considers the basic image features employed by most DNA micro array analysis techniques. Experimental results indicate that the proposed metric can identify and differentiate important and unimportant changes in DNA micro array images.

Visually lossless coding is a method through which an image is coded with numerical losses that are not noticeable by visual inspection. Contrary to numerically lossless coding, visually lossless coding can achieve high compression ratios. In general, visually lossless coding is approached from the point of view of the encoder, i.e., as a procedure devised to generate a compressed code stream from an original image. If an image has already been encoded to a very high fidelity (higher than visually lossless - perhaps even numerically lossless), it is not straightforward to create a just visually lossless version without fully re-encoding the image. However, for large repositories, re-encoding may not be a suitable option. A visually lossless decoder might be useful to decode, or to parse and transmit, only the data needed for visually lossless reconstruction. This work introduces a decoder for JPEG 2000 code streams that identifies and decodes the minimum amount of information needed to produce a visually lossless image. The main insights behind the proposed method are to estimate the variance of the code blocks before the decoding procedure, and to determine the visibility thresholds employing a well-known model from the literature. The main advantages are faster decoding and the possibility to transmit visually lossless images employing minimal bit rates.

Computed Tomography (CT) devices irradiate a (human) body with controlled amounts of X-ray to produce an image where different substance (lung, tissue, vessels, etc.) can be identified unequivocally. Commonly, CT devices also capture areas that do not belong to the human body. Such areas are referred to as air pixels, and may contain imaging artifacts. The air pixels are irrelevant for the medical diagnostic and provoke an important degradation in coding efficiency. In order to improve coding performance, we propose an air filtering technique based on a thresholding in the wavelet domain. The thresholds are determined through the existing relation between wavelet coefficients and image samples, which can be expressed in terms of a probability function. The proposed scheme filters air pixels in the wavelet domain by removing coefficients that are below a given threshold. The thresholds are estimated for different resolution levels and subbands, obtaining a probability of 70% to correctly filter air pixels. Although the proposed technique introduces an slight distortion in terms of RMSE in the biological area, this distortion is negligible compared with the state-of-the-art HDCS filter. These results suggest that the rate-distortion coding performance of our proposal and HDCS outperform significantly the coding performance of JPEG2000. In addition, Table 1 provides the RMSE of the HDCS and our proposal when compared with the original image, indicating that our proposal introduces much less RMSE distortion.

Summary form only given. X-ray angiographic (angio) images are widely used for identifying irregularities in the vascular system. Because of their high spatial resolution and the increasingly amount of X-ray angio images generated, compression of these images is becoming increasingly appealing. In this paper, we introduce a diagnostically lossless compression scheme for X-ray angio images. The coding scheme relies on a novel method based on ray casting and a-shapes for distinguishing the clinically relevant Region of Interest from the background. The background is then suppressed to increase data redundancy, allowing to achieve a higher coding performance. Experimental results suggest that the proposed scheme correctly identifies the Region of Interest in X-ray angio images and achieves more than 2 bits per pixel reduction in average as compared to the case of compression with no background suppression. Results are reported here for 20 out of 25 images compressed using various lossless compression methods.

X-ray angiography (angio) images are widely used to identify irregularities in the vascular system. Because of their high spatial resolution and the increasing amount of X-ray angio images generated, compression of these images is becoming paramount. In this paper, we propose a diagnostically lossless compression method based on automatic segmentation using ray-casting and α-shapes. The diagnostically relevant Region of Interest is separated from the background by exploiting the inherent symmetrical features of the image. The background-suppressed images are then losslessly encoded using DICOM-compliant lossless compression methods. Experimental results suggest that the proposed method correctly identifies the Region of Interest in X-ray angio images with an average segmentation accuracy of 98.4% and achieves more than 2 bits per pixel improvement on compression performance as compared to lossless compression with no background suppression.

Computed tomography (CT) is a noninvasive medical test obtained via a series of X-ray exposures resulting in 3-D images that aid medical diagnosis. Previous approaches for coding such 3-D images propose to employ multicomponent transforms to exploit correlation among CT slices, but these approaches do not always improve coding performance with respect to a simpler slice-by-slice coding approach. In this paper, we propose a novel analysis which accurately predicts when the use of a multicomponent transform is profitable. This analysis models the correlation coefficient r based on image acquisition parameters readily available at acquisition time. Extensive experimental results from multiple image sensors suggest that multicomponent transforms are appropriate for images with correlation coefficient r in excess of 0.87.

Abstract—The efficient transmission of large resolution im-ages and, in particular, the interactive transmission of images in a client-server scenario, is an important aspect for many applications. Among the current image compression standards, JPEG2000 excels for its interactive transmission capabilities. In general, three mechanisms are employed to optimize the trans-mission of images when using the JPEG2000 Interactive Protocol (JPIP): 1) packet re-sequencing at the server; 2) prefetching at the client; and 3) proxy servers along the network infrastructure. To avoid the congestion of the network, prefetching mechanisms are not commonly employed when many clients within a local area network (LAN) browse images from a remote server. Aimed to maximize the responsiveness of all the clients within a LAN, this work proposes the use of prefetching strategies at the proxy server –rather than at the clients. The main insight behind the proposed prefetching strategies is a user-navigation model and a semantic map that predict the future requests of the clients. Experimental results indicate that the introduction of these strategies into a JPIP proxy server enhances the browsing experience of the end-users notably. Index Terms—Interactive image transmission, JPEG2000, JPIP, prefetching strategies, user-navigation model, semantic map. I.

Preclinical imaging is a key enabling technology for medical research, such as in drug discovery, cancer detection, or osteoporosis screening. Europe is investing a significant amount of resources in a distributed phenotype study, carried out using Micro Computed Tomography (CT) images, which are acquired at very high resolutions to ease the detection of skeleton malformations. Unfortunately, like other CT images, MicroCT images commonly contain a notable amount of noise emitted by the acquisition device, which hinders the encoding of such images. In order to improve coding performance, we propose to use the Hounsfield Scale to establish a relationship between CT values and biological tissue together with an air-filtering approach, which modifies only samples located outside the biological area without penalizing the usefulness of the images to clinical experts, nor the visual perception of images. The proposed filter sets all samples having value lower than a specific threshold T to a constant figure.

DNA micro arrays are useful to identify the function and regulation of a large number of genes in a single experiment, even whole genomes. In this work, we analyze the relationship between DNA micro array image histograms and the compression performance of loss less JPEG2000. Also, a reversible transform based on histogram swapping is proposed. Intensive experimental results using different coding parameters are discussed. Results suggest that this transform improves previous loss less JPEG2000 results on all DNA micro array image sets.

Embedded quantization is a mechanism employed by lossy image coding systems to successively refine the distortion of an image. Commonly, it is conducted through a uniform scalar dead zone quantizer (USDQ) together with a bitplane coding strategy (BPC). Although this scheme is convenient for current hardware architectures and achieves competitive coding performance, it establishes the embedded quantizer without allowing major variations. This paper studies the design of non-restricted embedded quantizers with the aim to determine a quantization scheme that provides (near-)optimal performance for the lossy compression of images at low rates. Results suggest that optimally designed quantization schemes can achieve slightly better performance than that of USDQ+BPC by employing a non-uniform quantizer that requires fewer quantization stages.

The efficient transmission of large resolution images is a key aspect in many applications to minimize the transmission costs and to enhance the browsing experience. Among the currently available standards for the coding and transmission of imagery, JPEG2000 excels for its superior coding performance and advanced capabilities. The JPEG2000 Interactive Protocol (JPIP) minimizes the amount of information transmitted in a client-server scenario. Nonetheless, JPIP does not provide mechanisms to re-use data already delivered to clients browsing the same image within a local network. Common HTTP proxy servers are not able to understand the syntax of JPIP, thus specialized JPIP proxy servers are put in practice. This work improves the capabilities of traditional JPIP proxy servers by means of a user-navigation model that, together with prefetching strategies, allows the server to anticipate (potential) future requests of clients. Experimental evidence indicates that the introduction of the navigational model into a JPIP proxy server enhances the browsing experience notably.

This paper presents a joint source/channel coding method in 3D-JPEG2000 for wireless transmission of 3D regions of interest (RoIs) in 3D medical images. The method provides unequal error protection by combining the error correction capabilities of channel codes with the 3D-JPEG2000 error resilience tools. It generates quality scalable and error-resilient code streams with 3D-RoI decoding capabilities. The robustness of the proposed method is evaluated over Rayleigh-fading channels with rate-compatible punctured turbo codes. Comparisons are made with the cases of equal error protection and unequal error protection. Evaluations results on real 3D medical images show a significant improvement in reconstruction quality of the received 3D-RoIs and 3D images, in terms of the average peak signal-to-noise ratio.

Rate allocation methods are used for variable bit rate (VBR) video transmission. The Fast rate allocation through STepest descent (FAST) is a rate allocation method that achieves valid solutions in scenarios where buffer and bandwidth may vary from client to client, respecting buffer limits and fulfilling real time processing requeriments in a JPEG2000 framework. This work restates the optimization problem and implements an expansion for FAST algorithm, allowing the adaptation to possible changes in channel bandwidth while video still transferring. The proposed method considers buffer's fullness, bandwidth changes and number of frames remainig to transfer in order to provide a valid solution. Experimental results suggest the validity of solutions whereas maintaining the initial version features.

Radiation exposure increases the risk of inducing cancer in a patient when Computed Tomography is performed. Radiologists reduce the radiation dose to minimize the risk of cancer, consequently, noise is introduced in the image, considerably penalizing its quality. To enhance the quality of the image, different noise filters are developed. The filtered noise can be discarded due to its insignificance for the medical diagnosis. In this research, a coding scheme for Computed Tomography with JPEG2000 including a noise filtering stage is proposed. The JPEG2000 standard is selected in our coding approach because: it is supported in DICOM, the communication protocol in the medical setting, it includes progressive lossy-to-lossless coding, and it incorporates JPIP, the interactive transmission protocol. Extensive experimental tests using different images suggest that noise filtering does not penalize the visual quality, but allows substantial improvements in coding performance.

Hyperspectral data lossy compression has not yet achieved global acceptance in the remote sensing community, mainly because it is generally perceived that using compressed images may affect the results of posterior processing stages. This possible negative effect, however, has not been accurately characterized so far. In this letter, we quantify the impact of lossy compression on two standard approaches for hyperspectral data exploitation: spectral unmixing, and supervised classification using support vector machines. Our experimental assessment reveals that different stages of the linear spectral unmixing chain exhibit different sensitivities to lossy data compression. We have also observed that, for certain compression techniques, a higher compression ratio may lead to more accurate classification results. Even though these results may seem counterintuitive, this work explains these observations in light of the spatial regularization and/or whitening that most compression techniques perform and further provides recommendations on best practices when applying lossy compression prior to hyperspectral data classification and/or unmixing.

Spectral transforms are widely used for the codification of remote-sensing imagery, with the Karhunen-Loêve transform (KLT) and wavelets being the two most common transforms. The KLT presents a higher coding performance than the wavelets. However, it also carries several disadvantages: high computational cost and memory requirements, difficult implementation, and lack of scalability. In this paper, we introduce a novel transform based on the KLT, which, while obtaining a better coding performance than the wavelets, does not have the mentioned disadvantages of the KLT. Due to its very small amount of side information, the transform can be applied in a line-based scheme, which particularly reduces the transform memory requirements. Extensive experimental results are conducted for the Airborne Visible/Infrared Imaging Spectrometer and Hyperion images, both for lossy and lossless and in combination with various hyperspectral coders. The results of the effects on Reed Xiaoli anomaly detection and k-means clustering are also included. The theoretical and experimental evidences suggest that the proposed transform might be a good replacement for the wavelets as a spectral decorrelator in many of the situations where the KLT is not a suitable option.

Remote browsing of images is receiving much attention lately, mostly in niche applications like geographical information systems or in the telemedicine scenery. Interactive transmission of compressed images has been identified as the most competitive approach, being JPIP, JPEG2000 Interactive Protocol, a key-enabler for these situations. Also, it has been reported that JPIP Proxy Servers help to increase the transmission performance, and that prefetching strategies help to lower responsiveness time. In this paper we contribute a smart JPIP Proxy Server that, thanks to a prefetching strategy undertaken during idle transmission times, largely improves the viewing experience of the final user because of its anticipation of future navigation requests. Experimental results are reported for a remote sensing and a medical environment, respectively performing panning and zoom in, showing enhanced performance in both cases.

Context-adaptive binary arithmetic coding (CABAC) is a popular approach to diminish the statistical redundancy of symbols emitted by bitplane image coders. The main idea behind CABAC is to set up appropriate context models for coefficients, and to adapt probability estimates for each context to the nonstationary statistical behavior of symbols as more data are coded. This works introduces a mathematical model to determine probability estimates conceived from a characterization of the signal's nature within wavelet subbands. The proposed model assumes stationary statistical behavior for emitted symbols, thus the context-adaptive process carried out by CABAC is avoided. Experimental results in the framework of JPEG2000 suggest 2% increment on coding efficiency.

Most sensors used for remote sensing (RS) purposes capture more than one component to seize different features from the Earth's surface. Usually, either multispectral images acquired for RS applications are corrected or the user/application determines valid regions within the image. Consequently, regions without information may emerge (no-dataregions). This letter proposes to encode multispectral images with no-data regions through the JPEG2000 framework, taking into account the lack of importance of these irrelevant regions. Experimental results, performed on data from real scenarios, suggest that the best approach analyzed is the shape-adaptive (SA) Karhunen-Loe¿ve transform to decorrelate the spectral redundancy and then the SA multicomponent JPEG2000. The coding-performance improvement over other coding systems considered (Binary Set Splitting with K-D Trees, SA Wavelet Difference Reduction, and SA TARP) is from 5 to 20 dB in signal-to-noise ratio energy.

The Karhunen-Loêve transform (KLT) is widely used in hyperspectral image compression because of its high spectral decorrelation properties. However, its use entails a very high computational cost. To overcome this computational cost and to increase its scalability, in this paper, we introduce a multilevel clustering approach for the KLT. As the set of different multilevel clustering structures is very large, a two-stage process is used to carefully pick the best members for each specific situation. First, several candidate structures are generated through local search and eigenthresholding methods, and then, candidates are further screened to select the best clustering configuration. Two multilevel clustering combinations are proposed for hyperspectral image compression: one with the coding performance of the KLT but with much lower computational requirements and increased scalability and another one that outperforms a lossy wavelet transform, as spectral decorrelator, in quality, cost, and scalability. Extensive experimental validation is performed, with images from both the AVIRIS and Hyperion sets, and with JPEG2000, 3D-TCE, and CCSDS-Image Data Compression recommendation as image coders. Experiments also include classification-based results produced by k-means clustering and Reed-Xiaoli anomaly detection.

The RKLT is a lossless approximation to the KLT, and has been recently employed for progressive lossy-to-lossless coding of hyperspectral images. Both yield very good coding performance results, but at a high computational price. In this paper we investigate two RKLT clustering approaches to lessen the computational complexity problem: a normal clustering approach, which still yields good performance; and a multi-level clustering approach, which has almost no quality penalty as compared to the original RKLT. Analysis of rate-distortion evolution and of lossless compression ratio is provided. The proposed approaches supply additional benefits, such as spectral scalability, and a decrease of the side information needed to invert the transform. Furthermore,since with a clustering approach, SERM factorization coefficients are bounded to a finite range, the proposed methods allow coding of large three dimensional images within JPEG2000.

Distortion estimation techniques are often employed in bitplane coding engines to minimize the computational load, or the memory requirements, of the encoder. A common approach is to determine distortion estimators that approximate the mean squared error decreases when data are successively coded and transmitted. Such estimators usually assume that coefficients are uniformly distributed in the quantization interval. Even though this assumption simplifies estimation, it does not exactly correspond with the nature of the signal. This work introduces new distortion estimators determined through a precise approximation of the coefficient's distribution within the quantization intervals. Experimental results obtained when our estimators are used for the post-compression rate-distortion optimization process of JPEG2000 suggest that they are able to approximate distortion with very high accuracy.

Progressive lossy-to-lossless methods for hyper-spectral image coding are becoming common in remote-sensing. However, as remote-sensing imagery is sometimes fed directly into an automated process, there are several alternative distortion measures directed to quantify the image quality with regard to how this process will perform. In this scenario, we investigate the quality evolution in the lossy regime of progressive lossy-to-lossless and perform a detailed evaluation.

Most sensors used in remote sensing applications produce multicomponent images. These images may contain areas without information (no-data regions). This paper introduces a scheme composed by three main stages to encode this kind of images. The first stage is devised to exploit the spectral and spatial redundancies through the KLT and DWT. The second stage applies either the 2D-BISK, or its 3D extension (3D-BISK), or the multi-component shape adaptive (SA) version of JPEG2000. In the last stage, two different rate-control methods to combine the components' bitstreams are proposed. Experiments, performed on data from real applications, suggest that the best approach is SA KLT and SA 2D-DWT to decor-relate the spectral and spatial redundancies respectively, and a 2D coding system, followed by a rate-control method. The SNR improvement is from 5 dB to 20 dB over other strategies.

Region of interest (ROI) coding is a feature of prominent image coding systems that enables the specification of different coding priorities to certain regions of the image. JPEG2000 provides ROI coding through two mechanisms: either modifying wavelet coefficients or using rate-distortion optimization techniques. Although ROI coding methods based on the modification of wavelet coefficients provide an excellent accuracy to delimit the ROI area (referred to as fine-grain accuracy), they significantly penalize the coding efficiency. On the other hand, methods based on rate-distortion optimization improve the coding efficiency but, so far, have not been able to achieve the intended fine-grain accuracy. This letter introduces two ROI coding methods that, using rate-distortion optimization techniques, achieve a fine-grain accuracy, comparable to the one obtained when wavelet coefficients are modified, and are competitive in terms of coding efficiency.

This paper presents prominent extensions that have been proposed for the Consultative Committee for Space Data Systems Recommendation for Image Data Compression (CCSDS-122-B-1). Thanks to the proposed extensions, the Recommendation gains several important featured advantages: It allows any number of spatial wavelet decomposition levels; it provides scalability by quality, position, resolution, and component; and it supports multi-/hyper-/ultraspectral data coding, allowing a spectral decorrelation if requested. As a consequence, compression performance is notably improved with respect to the Recommendation for a large variety of remote sensing images, both monoband and multi-/hyper-/ultraspectral images. Reported results for hyperspectral data suggest that our proposal is competitive with the JPEG2000 standard.

Region Of Interest (ROI) coding is a mechanism deployed in several image coding systems to enable different degrees of coding priority to specific regions of the image. JPEG2000 standard provides two ROI coding methods. However, both of them are based in mechanisms that scale the quantized coefficients. This compels to encode the additional bit-planes needed for the scaling, causing a penalization in the overall coding performance, and the ROIs can not be modified without a complete re-encoding of the image. This paper introduces two ROI coding methods that use Rate-distortion optimization techniques toprioritize arbitrary ROIs over the rest of the image without penalizing the overall coding performance. Experimental results suggest that the proposed methods achieve a close tooptimal accuracy, improving the Implicit ROI coding method in terms of ROI rate distortion performance.

In this paper a modification on the file syntax of the CCSDS Recommendation for Image Data Coding (CCSDS-122-B-1) is presented. With respect to the Recommendation, the proposed modification provides, among others, scalability by quality, spatial location, resolution, and component, and also allows multicomponent data coding. In addition, experimental results show that our proposal produces a meaningful improvement over the performance of the Recommendation. Reported results for hyperspectral data are alsocompetitive with JPEG2000, the reference technique.

The aim of this paper is to enhance the coding performance obtained for images that contain regions without information, here named as no-data regions, within the JPEG2000 framework. In remote sensing and in telemedicine, no-data regions can be produced due to several factors, such as geometric corrections, overlapping of successive layers of information, the interest of the user/application in only certain regions within the image, etc. Most coding systems are not devised to consider such regions separately from the rest of the image, sometimes causing a loss in the coding efficiency. We propose four techniques that address this issue. Experimental results, performed on data from real applications, suggest that the proposed techniques can achieve, in some cases, a PSNR improvement of about 2.5 dB.

Quality scalability is a fundamental feature of JPEG2000, achieved through the use of quality layers that are optimally formed in the encoder by rate-distortion optimization techniques. Two points, related with the practical use of quality layers, may need to be addressed when dealing with JPEG2000 code-streams: 1) the lack of quality scalability of code-streams containing a single or few quality layers and 2) the rate-distortion optimality of windows of interest transmission. Addressing these two points, this paper proposes a mechanism that, without using quality layers, provides competitive quality scalability to code-streams. Its main key-feature is a novel characterization of the code-blocks rate-distortion contribution that does not use distortion measures based on the original image, or related with the encoding process. Evaluations against the common use of quality layers, and against a theoretical optimal coding performance when decoding windows of interest or when decoding the complete image area, suggest that the proposed method achieves close to optimal results.

Quality scalability is a fundamental feature of JPEG2000, achieved through the use of quality layers. Two points, related with the use of quality layers, may need to be addressed when dealing with JPEG-2000 code-streams: 1) the lack of quality scalability of single quality layer code-streams, and 2) the non rate-distortion optimality of windows of interest transmission. This paper introduces a new rate control method that can be applied to already encoded code-streams, addressing these two points. Its main key-feature is a novel characterization that can fairly estimate the rate-distortion slope of the coding passes of code-blocks without using any measure based on the original image or related with the encoding process. Experimental results suggest that the proposed method is able to supply quality scalability to already encoded code-streams achieving a near-optimal coding performance. The low computational costs of the method makes it suitable for use in interactive transmissions.

Geographic Information Systems (GIS) and Remote Sensing (RS) applications are becoming an important issue for the territorial management, governmental and research projects, and for many fields of our society. A characteristic of such applications is the displaying of successive layers of information that, in some cases, may overlap areas of the displayed images that are eventually never showed to the final user of the application. Even though these overlapped areas are of null interest, the coding of these images considers the complete area of the image, and thus the coding performance of the compression system is penalized. This paper introduces a novel use of the Region Of Interest (ROI) coding techniques to overcome the drawbacks of the map overlapping in GIS and RS applications. The proposed approach is based on a ROI coding method defined for the JPEG2000 standard that efficiently improves the coding performance and keeps JPEG2000 compliance.

This letter introduces a new rate control method devised to provide quality scalability to JPEG2000 codestreams containing a single or few quality layers. It is based on a Reverse subband scanning Order and a coding passes Concatenation (ROC) that does not use distortion measures based on the original image. The proposed ROC method allows a flexible rate control when the image has already been encoded, using negligible computational resources and obtaining the same efficiency as when using quality layers. Besides, the proposed ROC can be used in the encoding process to reduce the coder complexity, avoiding to encode unnecessary coding passes and achieving a competitive performance in terms of mean-square error (MSE)

Summary form only given. We adapted shape-adaptive coding and the BISK algorithm to new sign and refinement encoders, with the novelty of encoding separately the refinement bits for each set of coefficients having the same prefix. This allows the algorithm to capitalize the refinement redundancy among each of these sets. The proposed sign and refinement encoders may be considered independently and may be integrated to other bit plane encoders. However, the search scheme suggests a new complete bit plane encoder defined by an exhaustive two-valued shape-adaptive search (ETSE). Even though ETSE does not include arithmetic coding, the coding performance of ETSE is competitive when compared to other wavelet-based encoders which include arithmetic coding. Furthermore, as BISK, ETSE may perform compression of images with non-regular boundary

The JPEG2000 standard does not specify how to perform the rate control strategy, needed either to achieve a target bitrate, or to construct quality layers. In this paper, an efficient rate control algorithm is described. It is based on the interleaving of coding passes and it has a low computational complexity. The proposed algorithm encodes only the coding passes included in the final codestream, and it fully avoids the need of any post compression rate distortion stage. Extensive experimental results show that the encoding performance of our method is competitive and similar to the optimal strategy. In addition, this proposal allows the extraction of a target bitrate from a codestream without the need of either knowing the original image, or of decoding any part of the codestream. The performance of this kind of extraction is equivalent to that obtained when decompressing a codestream organized in quality layers.

This study measures the effect of lossy image compression on the digital classification of crops and forest areas. A hybrid classification method using satellite images and other variables has been used. The results contribute interesting new data on the influence of compression on the quality of the produced cartography, both from a "by pixel" perspective and regarding the homogeneity of the obtained polygons. The classified area in classifications only carried out with radiometric variables or with NDVI and humidity (for crops) increases as image compression increases, although the increase is smaller for JPEG2000 formats and for crops. On the other hand, the classified area decreases in classifications which also take into account topoclimatic variables (for forests). Overall image accuracy diminishes at high compression ratios (CR), although the point of inflection occurs at different CR depending on the compression format. As a rule, the JPEG2000 format gives better results quantitatively for forests (accuracy and classified area) and visually (images with less "salt and pepper" effect) for both land covers.

This paper describes a study regarding the influence of a watermarking scheme based on near-lossless JPEG2000 compression in the performance of lossless or near-lossless image compression methods. Due to the nature of ultraspectral imaging applications, the imperceptibility property becomes more important than capacity or robustness, which determines the design of the watermarking scheme. Four strategies for band watermarking are discussed, depending on which bands are selected for embedding the mark and how. Preliminary results show that both intra-granule and inter-granule band capacity and imperceptibility are very variable, thus an adaptive watermarking band strategy must be used. On the other hand, the proposed watermarking scheme does not alter the obtained compression ratio for watermarked images when compared to the original ones, for a reasonable capacity and an imperceptible quality loss.

This paper provides a study concerning the suitability of well-known image coding techniques originally devised for lossy compression of still natural images when applied to lossless compression of ultraspectral sounder data. An ultraspectral sounder generates an unprecedented amount of 3D data, consisting of two spatial and one spectral dimensions; with ultraspectral sounder data, better inference of atmospheric, cloud and surface parameters is feasible. Here we present the experimental results of five widespread wavelet-based coding techniques, namely EZW, IC, SPIHT, JPEG2000 and CCSDS-IDC. Since the considered still image coding techniques are 2D in nature, but the ultraspectral data is 3D, a preprocessing step is applied to convert the two spatial dimensions into a single one. We are also interested in analyzing the benefits of applying some pre-processing step (e.g., linear prediction or bias adjusted reordering) prior to the coding process in order to further exploit the spectral correlation, which is much stronger than the spatial correlation.

This paper deals with the encoding of high resolution images for remote sensing and geographic information systems applications. We are currently investigating the suitability of several still image coding techniques for this kind of applications. We present results for an adapted and modified version of the CCSDS-ILDC technique. In addition to evaluate its compression factor and quality of recovery with respect to the latest still image coding standard JPEG2000, we also consider whether this technique may fulfill the particular functionalities requested by remote sensing users

With the aim of obtaining a valid compression method for remote sensing and geographic information systems, and because comparisons among the different available techniques are not always performed in a sufficient fair manner, we are currently developing a JAVA framework for evaluating several still image coding techniques. In addition to properly choose the best suitable technique according to compression factor and quality of recovery, it is expected that this setting will let us introduce the particular functionalities requested by this kind of applications.