Joan Serra-Sagristà

Autonomous University of Barcelona | UAB · Department of Information and Communication Engineering

Observatories are producing astronomical image data at quickly increasing rates. As a result, the efficiency of the compression methods employed is critical to meet the storage and distribution requirements of both observatories and scientists. This paper presents a novel lossy compression technique that is able to preserve the results of photometr...

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the TLSTM to capture complex temporal dependencies in r...

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 de...

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 with...

The Consultative Committee for Space Data Systems (CCSDS) recently published a lossless compression standard for housekeeping and telemetry. These data are critical for the safe and productive operation of virtually all remote sensing missions, including Earth observation. The new standard, CCSDS 124.0-B-1 “Robust Compression of Fixed-Length Housek...

Fixed-quality image compression is a coding paradigm where the tolerated introduced distortion is set by the user. This paper proposes a novel fixed-quality compression method for remote sensing images. It is based on a neural architecture we have recently proposed for multirate satellite image compression. In this paper, we show how to efficiently...

Each new generation of telescope produces increasingly larger astronomical data volumes, which are expected to reach the order of exabytes in the next decade. Effective and fast data compression methods are paramount to help the scientific community contain storage costs and improve transmission times. Astronomical data differs significantly from n...

Two key hurdles to the adoption of Machine Learning (ML) techniques in hyperspectral data compression are computational complexity and scalability for large numbers of bands. These are due to the limited computing capacity available in remote sensing platforms and the high computational cost of compression algorithms for hyperspectral data, especia...

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...

sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iquaflow is a framework that provides a set of tools to assess image quality. The user can add custom metrics that can be easily integrated and a set of unsupervised methods is offered by default. Furthermore, iquaflow measures quality by using the perfo...

IQUAFLOW is a new image quality framework that provides a set of tools to assess image quality. The user can add custom metrics that can be easily integrated. Furthermore, iquaflow allows to measure quality by using the performance of AI models trained on the images as a proxy. This also helps to easily make studies of performance degradation of se...

Pilitropic and flagellotropic phages adsorb to bacterial pili and flagella. These phages have long been used to investigate multiple aspects of bacterial physiology, such as the cell cycle control in the Caulobacterales. Targeting cellular appendages for adsorption effectively constrains the population of infectable hosts, suggesting that phages ma...

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...

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 Consultative Committee for Space Data Systems (CCSDS) 123.0-B-2 standard emerges as an interesting option to compress multi- and hyperspectral images on-board satellites, su...

A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lo...

A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lo...

Designing small and efficient mobile neural networks is difficult because the challenge is to determine the architecture that achieves the best performance under a given limited computational scenario. Previous lightweight neural networks rely on a cell module that is repeated in all stacked layers across the network. These approaches do not permit...

This letter proposes methods to improve data size and access time for k²-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 ²-raster, the size of the hyperspectral data was reduced by up to 52% compar...

The Consultative Committee for Space Data Systems (CCSDS) published the CCSDS 123.0-B-2, “Low- Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression” standard. This standard extends the previous issue, CCSDS 123.0-B-1, which supported only lossless compression, while maintaining backward compatibility. The main nov...

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 the tabled variant of ANS are known to provide varying performances depending on their internal design. In this paper, we present a method...

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...

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...

Efficient high-throughput (HT) compression algorithms are paramount to meet the stringent constraints of present and upcoming data storage, processing, and transmission systems. In particular, latency, bandwidth and energy requirements are critical for those systems. Most HT codecs are designed to maximize compression speed, and secondarily to mini...

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 ra...

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 b...

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...

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...

In this paper we provide a method to obtain tight lower bounds on the minimum redundancy achievable by a Huffman code when the probability distribution underlying an alphabet is only partially known. In particular, we address the case where the occurrence probabilities are unknown for some of the symbols in an alphabet. Bounds can be obtained for a...

Predictive image coding systems yield high compression performance at low computational complexity, and are therefore popular in standards and prominent coding techniques for both lossless and near-lossless compression. However, few prediction-based coding techniques include rate-control approaches because of the difficulty in properly combining th...

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...

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...

Marlin 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. Th...

This paper describes the emerging Issue 2 of the CCSDS-123.0-B standard for low-complexity compression of multispectral and hyperspectral imagery, focusing on its new features and capabilities. Most significantly, this new issue incorporates a closed-loop quantization scheme to provide near-lossless compression capability while still supporting los...

In this paper we provide a method to obtain tight bounds on the minimum redundancy achievable by a Huffman code when the probability distribution underlying an alphabet is only partially known. In particular, we address the case where the occurrence probabilities are unknown for some of the symbols in an alphabet. Bounds can be obtained for alphabe...

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 est...

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 c...

Marlin 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...

The evolution of the optical and of the sounding interferometer instruments along with the increase of the spaceborne storage capacity allows for the acquisition of large data volumes. However, the strongly limited downlink bandwidth unveils an insufficient on-board storage capacity, and the on-the-ground storage and dissemination are also conteste...

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...

Progressive Lossy-to-Lossless (PLL) coding techniques enable a gradual quality improvement of the recovered images, starting from a coarse approximation up to a perfect reconstruction. PLL is becoming a widespread approach in several scenarios, in particular, for compression of hyperspectral images. In this paper we assess the suitability of Regres...

The Infrared Atmospheric Sounding Interferometer (IASI), implemented on the MetOp satellite series, represents a significant step forward in atmospheric forecast and weather understanding. The instrument provides infrared soundings of unprecedented accuracy and spectral resolution to derive humidity and atmospheric temperature profiles, as well as...

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 fai...

This article extends a rate-allocation method based on the near-lossless-rate (NLR) complexity that is designed to operate on-board spacecrafts, to include support for distortion scaling factors, such as those that are needed to code multi- and hyperspectral image when a spectral transform is employed. In this article, the conditions to achieve glo...

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 mos...

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 informatio...

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...

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 impro...

Linear multi-component transforms (MCTs) are commonly employed for enhancing the coding performance for the compression of natural color images. Popular MCTs such as the RGB to Y'CbCr transform are not optimized specifically for any given input image. Data-dependent transforms such as the Karhunen-Loève Transform (KLT) or the Optimal Spectral Trans...

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 ne...

Most compression methods for hyperspectral images have been optimized to minimize mean squared errors. However, this kind of compression method may not retain all discriminant information, which is important if hyperspectral images are to be used to distinguish among classes. In this paper, we propose a two-stage compression method for hyperspectra...

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 recent...

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 WSIs. However, coding times remain relatively high due to the high number (35) of...

X-ray angiography images are widely used to identify irregularities in the vascular system. Because of their high spatial resolution and the large amount of images generated daily, coding of X-ray angiography images is becoming essential. This paper proposes a diagnostically lossless coding method based on automatic segmentation of the focal area u...

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 co...

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 thi...

Preclinical micro-computed tomography (microCT) images are of utility for 3D morphological bone evaluation, which is of great interest in cancer detection and treatment development. This work introduces a compression strategy for microCTs that allocates specific substances in different Volumes of Interest (VoIs). The allocation procedure is conduct...

A number of compression methods have been used to compress hyperspectral images. However, these methods may fail to retain all the discriminant characteristics of hyperspectral images since some discriminant features may not be high in signal energy. Also, it has been reported that compression may improve classification performance in some cases. I...

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 PO...