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 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.

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.

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 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%.

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.

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.

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.

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.

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 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.