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Real-life room-escape games are ludic activities in which participants enter a room in order to get out of it only after solving some riddles. In this paper, we explain a Room Escape teaching experience developed in the Engineering School at Universitat Autònoma de Barcelona. The goal of this activity is to increase student's motivation and to improve their learning on two courses of the second year in the "Computer Engineering degree: Computer Networks and Information and Security". [Paper presented at the International Conference on University Teaching and Innovation (CIDUI): Learning and Teaching Innovation Impacts (9th, Bellaterra, Barcelona, Spain, Jul 5-7 2016.)]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

This letter proposes a near-lossless coder for hyperspectral images. The coding technique is fully embedded and minimizes the distortion in the $l_2$-norm initially and in the l_\infty$-norm subsequently. Based on a two-stage near-lossless compression scheme, it includes a lossy and a near-lossless layer. The novelties are the observation of the convergence of the entropy of the residuals in the original domain and in the spectral–spatial transformed domain and an embedded near-lossless layer. These contributions enable a progressive transmission while optimizing both signal-to-noise ratio (SNR) and peak absolute error (PAE) performance. The embeddedness is accomplished by bitplane encoding plus arithmetic encoding. Experimental results suggest that the proposed method yields a highly competitive coding performance for hyperspectral images, outperforming multicomponent JPEG2000 for the l_\infty$-norm and pairing its performance for the l_2. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

We review the state of the art in DNA microarray image compression and provide original comparisons between standard and microarray-specific compression techniques that validate and expand previous work. First, we describe the most relevant approaches published in the literature and classify them according to the stage of the typical image compression process where each approach makes its contribution, and then we summarize the compression results reported for these microarray-specific image compression schemes. In a set of experiments conducted for this paper, we obtain new results for several popular image coding techniques that include the most recent coding standards. Prediction-based schemes CALIC and JPEG-LS are the best-performing standard compressors, but are improved upon by the best microarray-specific technique, Battiato's CNN-based scheme. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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 κ-means clustering and Reed-Xiaoli anomaly detection. [ABSTRACT FROM AUTHOR]

The capacity of the downlink channel is a major bottleneck for applications based on remote sensing hyperspectral imagery (HSI). Data compression is an essential tool to maximize the amount of HSI scenes that can be retrieved on the ground. At the same time, energy and hardware constraints of spaceborne devices impose limitations on the complexity of practical compression algorithms. To avoid any distortion in the analysis of the HSI data, only lossless compression is considered in this study. This work aims at finding the most advantageous compression–complexity trade-off within the state of the art in HSI compression. To do so, a novel comparison of the most competitive spectral decorrelation approaches combined with the best performing low-complexity compressors of the state is presented. Compression performance and execution time results are obtained for a set of 47 HSI scenes produced by 14 different sensors in real remote sensing missions. Assuming only a limited amount of energy is available, obtained data suggest that the FAPEC algorithm yields the best trade-off. When compared to the CCSDS 123.0-B-2 standard, FAPEC is 5.0 times faster and its compressed data rates are on average within 16% of the CCSDS standard. In scenarios where energy constraints can be relaxed, CCSDS 123.0-B-2 yields the best average compression results of all evaluated methods. [ABSTRACT FROM AUTHOR]

This paper examines the various variable-length encoders that provide integer encoding to hyperspectral scene data within a k 2 -raster compact data structure. This compact data structure leads to a compression ratio similar to that produced by some of the classical compression techniques. This compact data structure also provides direct access for query to its data elements without requiring any decompression. The selection of the integer encoder is critical for obtaining a competitive performance considering both the compression ratio and access time. In this research, we show experimental results of different integer encoders such as Rice, Simple9, Simple16, PForDelta codes, and DACs. Further, a method to determine an appropriate k value for building a k 2 -raster compact data structure with competitive performance is discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k 2 -raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k 2 -raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding. [ABSTRACT FROM AUTHOR]

Hyperspectral images are depictions of scenes represented across many bands of the electromagnetic spectrum. The large size of these images as well as their unique structure requires the need for specialized data compression algorithms. The redundancies found between consecutive spectral components and within components themselves favor algorithms that exploit their particular structure. One novel technique with applications to hyperspectral compression is the use of spectral graph filterbanks such as the GraphBior transform, that leads to competitive results. Such existing graph based filterbank transforms do not yield integer coefficients, making them appropriate only for lossy image compression schemes. We propose here two integer-to-integer transforms that are used in the biorthogonal graph filterbanks for the purpose of the lossless compression of hyperspectral scenes. Firstly, by applying a Triangular Elementary Rectangular Matrix decomposition on GraphBior filters and secondly by adding rounding operations to the spectral graph lifting filters. We examine the merit of our contribution by testing its performance as a spatial transform on a corpus of hyperspectral images; and share our findings through a report and analysis of our results. [ABSTRACT FROM AUTHOR]

This article studies the performance impact related to different parameter choices for the new CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression standard. This standard supersedes CCSDS-123.0-B-1 and extends it by incorporating a new near-lossless compression capability, as well as other new features. This article studies the coding performance impact of different choices for the principal parameters of the new extensions, in addition to reviewing related parameter choices for existing features. Experimental results include data from 16 different instruments with varying detector types, image dimensions, number of spectral bands, bit depth, level of noise, level of calibration, and other image characteristics. Guidelines are provided on how to adjust the parameters in relation to their coding performance impact. [ABSTRACT FROM AUTHOR]

The intricate interplay between gut microbiota and the host is pivotal in maintaining homeostasis and health. Dietary tryptophan (TRP) metabolism initiates a cascade of essential endogenous metabolites, including kynurenine, kynurenic acid, serotonin, and melatonin, as well as microbiota-derived Trp metabolites like tryptamine, indole propionic acid (IPA), and other indole derivatives. Notably, tryptamine and IPA, among the indole metabolites, exert crucial roles in modulating immune, metabolic, and neuronal responses at both local and distant sites. Additionally, these metabolites demonstrate potent antioxidant and anti-inflammatory activities. The levels of microbiota-derived TRP metabolites are intricately linked to the gut microbiota's health, which, in turn, can be influenced by age-related changes. This review aims to comprehensively summarize the cellular and molecular impacts of tryptamine and IPA on health and aging-related complications. Furthermore, we explore the levels of tryptamine and IPA and their corresponding bacteria in select diseased conditions, shedding light on their potential significance as biomarkers and therapeutic targets. • Microbiota-derived tryptophan (TRP) metabolites are important in health and disease. • Level of TRP metabolites modulated by the gut microbiota content • Tryptamine and IPA are pivotal metabolites in the complex interplay between gut microbiota and the host. • These metabolites regulate immune response and oxidative stress and play a vital role in age-related diseases. [ABSTRACT FROM AUTHOR]