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.

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.

We propose a compression encoding method to perceptually optimize the image quality based on a novel quality metric, which emulates how the human visual system form opinion of a compressed image. Compared to the existing perceptual-optimized compression methods, which usually aim to minimize the detectability of compression artifacts and are sub-optimal in visually lossless regime, the proposed encoder aims to operate in the visually lossy regime. We implement the proposed encoder within the JPEG 2000 standard, and demonstrate its advantage over both detectability-based and conventional MSE encoders.

In this paper, we propose a visual discrimation model to enable perceptually optimized JPEG200 compression for both near-threshold and supra-threshold regimes. The performance of the proposed approach is validated by comparing it to the conventional Mean Squared Error (MSE)-optimized compression.

We propose a novel Coding Unit (CU) level luma-chroma perceptually adaptive quantization technique for HEVC; we name this technique C-BAQ. C-BAQ is designed to improve upon the luma-only adaptive perceptual quantization method adopted by JCT-VC. This luma-only perceptual quantization technique, named AdaptiveQP, has demonstrated coding improvements compared with Uniform Reconstruction Quantization; it increases or decreases the Quantization Parameter (QP) of an entire 2N×2N CU based on the spatial activity of raw samples in the constituent luma Coding Block (CB). AdaptiveQP does not take into account the spatial activity of raw samples in the corresponding chroma Cb and chroma Cr CBs, however, which constitutes a significant shortcoming of the method. In contrast to AdaptiveQP, the proposed C-BAQ technique accounts for the spatial activity of the raw samples in both the luma CBs and also the chroma CBs when modifying the QP at the CU level. The primary objective with C-BAQ is as follows: to significantly decrease bitrates, in comparison with AdaptiveQP, without incurring a discernible loss of reconstruction quality.

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.

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.

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.

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.