Compression de données

Résumé

La compression de données ou codage de source est l'opération informatique consistant à transformer une suite de bits A en une suite de bits B plus courte pouvant restituer les mêmes informations, ou des informations voisines, en utilisant un algorithme de décompression. C'est une opération de codage qui raccourcit la taille (de transmission, de stockage) des données au prix d'un travail de compression. Celle-ci est l'opération inverse de la décompression. Un algorithme de compression sans perte restitue après décompression une suite de bits strictement identique à l'originale. Les algorithmes de compression sans perte sont utilisés pour les archives, les fichiers exécutables ou les textes. Pour la compression de données sans pertes, on distingue principalement le codage entropique et le codage algorithmique. Le codage entropique est fondé sur des a priori quant à la source. On doit, par exemple, pour le codage de Huffman, transmettre une table de probabilités des symboles de la sour

Source officielle

https://fr.wikipedia.org/wiki/Compression_de_donn%C3%A9es

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Study of hydrogen storage and compression

Nicolas Jean Matet

The goal of this rapport is to present an overview of the different hydrogen storage technologies, currently available for industrial application or at earlier stages of development. In order to identify the water and mechanical work requirements for compression of hydrogen, flow sheets of several compression process are realized. In order to decrease the work needed for hydrogen compression, realizing part of the compression with a metal-hydride compressor and integrating the compression process with a solar hydrogen production plant was study. It shows that metal-hydride pre-compression of hydrogen using waste heat before mechanical compression has the potential to lower the mechanical work consumption by about 15%compared to multi-stage mechanical compression alone.

2014

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In this paper, we study an emerging class of neural networks, the Morphological Neural networks, from some modern perspectives. Our approach utilizes ideas from tropical geometry and mathematical morphology. First, we state the training of a binary morphological classifier as a Difference-of-Convex optimization problem and extend this method to multiclass tasks. We then focus on general morphological networks trained with gradient descent variants and show, quantitatively via pruning schemes as well as qualitatively, the sparsity of the resulted representations compared to FeedForward networks with ReLU activations as well as the effect the training optimizer has on such compression techniques. Finally, we show how morphological networks can be employed to guarantee monotonicity and present a softened version of a known architecture, based on Maslov Dequantization, which alleviates issues of gradient propagation associated with its "hard" counterparts and moderately improves performance.

IEEE2021

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On the Use of Masking Models for Image and Audio Watermarking

Justin Picard, Arnaud Robert

In most watermarking systems, masking models, inherited from data compression algorithms, are used to preserve fidelity by controlling the perceived distortion resulting from adding the watermark to the original signal. So far, little attention has been paid to the consequences of using such models on a key design parameter: the robustness of the watermark to intentional attacks. The goal of this paper is to demonstrate that by considering fidelity alone, key information on the location and strength of the watermark may become available to an attacker; the latter can exploit such knowledge to build an effective mask attack. First, defining a theoretical framework in which analytical expressions for masking and watermarking are laid, a relation between the decrease of the detection statistic and the introduced perceptual distortion is found for the mask attack. The latter is compared to the Wiener filter attack. Then, considering masking models widely used in watermarking, experiments on both simulated and real data (audio and images) demonstrate how knowledge on the mask enables to greatly reduce the detection statistic, even for small perceptual distortion costs. The critical tradeoff between robustness and distortion is further discussed, and conclusions on the use of masking models in watermarking drawn.

2005

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