Data compression

Related publications (100)

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

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

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

(Geometry Aware) Deep Learning-based Omnidirectional Image Compression

Yamin Sepehri

Omnidirectional images are the spherical visual signals that provide a wide, 360◦, view of a scene from a specific position. Such images are becoming increasingly popular in fields like virtual reality and robotics. Compared to conventional 2D images, the storage and badwidth requirements of omnidirectional signals are much higher, due to the specific nature of them. Thus, there is a need for image compression schemes to reduce the dedicated storage space of omnidirectional images. Image compression algorithms can be broadly classified into two groups: lossless and lossy. Lossless schemes are able to reconstruct the exact original data but they cannot reduce the size beyond a specific criteria. Lossy methods are generally better solutions if they do not add a high visual distortion to the reconstructed image, as long as they provide a decent compression rate. If a planar, lossy image compression scheme is applied on omnidirectional images, some problems show up. It is possible to apply a planar compression scheme on a projected version of a 360◦image; however, in these projection schemes (such as equirectangular projection) the sampling rate is different in the poles and the center. Consequently, the filters of the planar compression schemes that do not consider this difference ends in suboptimal result and distortions in the reconstructed images. Recently, with the success of deep neural networks in many image processing tasks, researchers began to use them for the image compression as well. In this study, we propose a deep learning-based method for the compression of omnidirectional images by combining some state of the art approaches from the deep learning-based image compression schemes and some special convolutional layers that take into account the geometry of the omnidirectional image. In comparison to the available methods, it is the first method that can be applied directly on the equirectangularly projected version of omnidirectional images and considers the geometry in the scheme and the layers themselves. To propose this method, different geometry-aware convolutional layers have been tried. We exploited various methods of downsampling and upsampling, such as spherical pooling layers, strided or transposed convolutions, bilinear interpolation, and pixel shuffle. In the end, a method is proposed that benefits from specific spherical convolutional layers which contain sampling methods considering the geometry of omnidirectional images. The sampling positions differ in the different heights of the image based on the nature of the projected omnidirectional image. Additionally, as it benefits from an iterative training method that calculates the residual between the output and input and feeds it again to the network as input of the next iteration, it can provide different compression rates with just one pass of training. Finally, it benefits from a novel method of patching that is well-aligned with the spherical convolution layers and helps the method to run efficiently without a need for a high computational power. The model was compared with a similar architecture without spherical convolutions and spherical patching and showed some improvements. The architecture has been optimized and improved and it has the potential to compete with popular image compression schemes such as JPEG especially in terms of reconstructing the colors.

2020

Source and channel coding using Fountain codes

Bertrand Ndzana Ndzana

The invention of Fountain codes is a major advance in the field of error correcting codes. The goal of this work is to study and develop algorithms for source and channel coding using a family of Fountain codes known as Raptor codes. From an asymptotic point of view, the best currently known sum-product decoding algorithm for non binary alphabets has a high complexity that limits its use in practice. For binary channels, sum-product decoding algorithms have been extensively studied and are known to perform well. In the first part of this work, we develop a decoding algorithm for binary codes on non-binary channels based on a combination of sum-product and maximum-likelihood decoding. We apply this algorithm to Raptor codes on both symmetric and non-symmetric channels. Our algorithm shows the best performance in terms of complexity and error rate per symbol for blocks of finite length for symmetric channels. Then, we examine the performance of Raptor codes under sum-product decoding when the transmission is taking place on piecewise stationary memoryless channels and on channels with memory corrupted by noise. We develop algorithms for joint estimation and detection while simultaneously employing expectation maximization to estimate the noise, and sum-product algorithm to correct errors. We also develop a hard decision algorithm for Raptor codes on piecewise stationary memoryless channels. Finally, we generalize our joint LT estimation-decoding algorithms for Markov-modulated channels. In the third part of this work, we develop compression algorithms using Raptor codes. More specifically we introduce a lossless text compression algorithm, obtaining in this way competitive results compared to the existing classical approaches. Moreover, we propose distributed source coding algorithms based on the paradigm proposed by Slepian and Wolf.

EPFL2009

Algorithms and methods for sparse approximation in structured dictionaries

Lorenzo Peotta

In the last decade we observed an increasing interaction between data compression and sparse signals approximations. Sparse approximations are desirable because they compact the energy of the signals in few elements and correspond to a structural simplification of the approximated signals. In the first part of this dissertation we consider the low bit rate image coding problem. The state of the art in image compression is to use two-dimensional (2-D) wavelets. The main advantage of wavelet bases lie in their multiscale nature and in their ability to sparsely represent functions that are piecewise smooth. Their main problem on the other hand, is that 2-D wavelets are not able to deal with the natural geometry of images, i.e. they cannot sparsely represent objects that are smooth away from regular submanifolds. We propose an approach based on building a sparse approximation of the edge part of images in a redundant geometrically inspired library of functions, followed by suitable coding techniques. The edge-oriented dictionary is built by anisotropically scaling, orienting and bending a generating function. The new bending transform generates elementary waveforms that efficiently approximate the edges; with few waveforms we are able to approximate long edges. The sparse approximations over the redundant dictionary are computed using a sub-optimal greedy strategy, as indeed finding the best m-terms non-linear approximations in general dictionaries is a NP-hard problem. Recently there has been an intense activity in the field of sparse approximations with redundant dictionaries, largely motivated by the practical performances of algorithms such as Matching Pursuit and Basis Pursuit. However, most of the theoretical results obtained so far are valid only for the restricted class of incoherent dictionaries. The second part of the dissertation investigates a new class of overcomplete dictionaries, called block incoherent dictionaries, where coherence can be arbitrarily large. We show that a simple greedy algorithm can correctly identify stable subdictionaries (called blocks) and demonstrate how one can use the extra coherence freedom for approximation purposes. With the aim of improving the sparse approximations over redundant dictionaries obtained with greedy algorithms, in the last part we develop a new decomposition algorithm characterized by the computational cost of MP and global optimization properties typical of the re-weighted minimum norm algorithms such as FOCUSS. We introduce the concept of sub-dictionary, a subset of the redundant dictionary which depends on the signal to be approximated, and we propose a sub-dictionary selection algorithm based on greedy techniques. The low computational cost greedy algorithm selects a sub-dictionary that contains the solution of more complex algorithms, which are utilized to obtain the approximation over the sub-dictionary. In the first part we studied image and video sparse approximation, however the concepts we present in the second part of the dissertation are general and can be applied to any kind of structured signal.

EPFL2007

EPFL1997

Spatio-temporal segmentation and object tracking

Visual information is taking a predominant place in our society. With the advent of digital technologies, visual information will find new applications in domains ranging from communication, commerce to entertainment. New functionalities will be required that permit an extended interaction with the visual information. In particular, this is the case for digital television and the related problem of video sequence compression. The extent of possible interactions depends on the manner in which the visual information is represented. Up to now, the canonical representation is used. Also referred to as the waveform representation, it is a technical artifact of the image capture procedure. This representation severely restricts the functionalities available to the end-user. The latter is unable to freely manipulate or customize the received visual information. In this dissertation, we propose to describe the visual information through a semantically meaningful representation. This representation directly derives from the scene content, which is decomposed in terms of its constituting objects. Consequently, the viewing process is totally disconnected from the image capture procedure. This permits full interactivity with the visual information, leading to enhanced functionalities for the end-user. The essence of this dissertation is to automatically define the objects forming the scene arid to automatically track them through the video sequence. Two main issues are identified: the initial segmentation of the objects and their tracking. The first issue deals with segmenting the scene into its constituent objects. This has to be performed on the basis of the information available in two consecutive frames. In order to solve the problem, a split-and-merge approach is used. First, the image is segmented into small, spatio-temporally homogeneous regions. This is achieved through a top-down approach where spatial, temporal and change information is combined. These regions are used as a starting point to define the objects forming the scene. A bottom-up approach is used which iteratively merges the regions. The propensity of the regions to form an object is assessed in terms of both spatial and temporal information. The second issue deals with segmenting and tracking the objects in the successive frames. The coherence between the successive segmentations is ensured by using past and current information. Also, the different objects composing the scene are identified throughout the video sequence. This identification relies on temporal, spatial and spatio-temporal features of the objects. The proposed representation of the visual information finds a natural application in video coding. A second generation video coding scheme is presented which combines compression efficiency with extended functionalities. In particular, scalable coding is achieved in terms of both scene content and object coding quality.

EPFL1997

Cell Seeding is a key factor for good regeneration in tissue engineering. In order to ensure uniform tissue regeneration, a system has to be found to distribute cells evenly within the scaffold that will carry them in the body. In this extent, this work try to find an accurate description of compression forced induced suction which is a seeding technique with reported satisfying seeding efficiency. This description will take the form of a numerical model with COMSOL Multiphysics and will allow us to maximize the seeding in terms of homogeneity. In order to find the best possible seeding, strains and velocities of the compression will be further analyzed. The scaffold material properties will also be examined in order to have the best possible material for that kind of applications.

2015

Though deep learning (DL) algorithms are very powerful for image processing tasks, they generally require a lot of data to reach their full potential. Furthermore, there is no straightforward way to impose various properties, given by the prior knowledge about the target task, on the features extracted by a DL model. Therefore, in this thesis we propose several techniques that rely on the power of graph representations to embed prior knowledge inside the learning process. This allows to reduce the solution space and leads to faster optimization convergence and higher accuracy in the representation learning. In our first work, inspired by the ability of a human to correctly classify rotated, shifted or flipped objects, we propose an algorithm that permits to inherently encode invariance to isometric transformations of objects in an image. Our DL architecture is based on graph representations and consists of three novel layers, which we refer to as graph convolutional, dynamic pooling and statistical layers. Our experiments on the image classification tasks show that our network correctly recognizes isometrically transformed objects even though such types of transformation are not seen by the network at training time. Standard DL techniques are typically not able to succeed in solving such a problem without extensive data augmentation. Then, we propose to exploit the properties of graph-based approaches to efficiently process images with various types of projective geometry. In particular, we are interested in increasingly popular omnidirectional cameras, which have a 360 degree field of view. Despite their effectiveness, such cameras create images with specific geometric properties, which require special techniques for efficient processing. We propose an efficient way of adjusting the weights of the graph edges to adapt the filter responses to the geometric image properties introduced by omnidirectional cameras. Our experiments prove that using the proposed graph with properly adjusted edge weights permits to reach better performance as compared to using regular grid graph with equal weights. Finally, the approach described above relies on the isotropic filters, which work well within our transformation invariant architecture for image classification. However, for other problems (e.g. image compression) or even when used without dynamic pooling and statistical layers that are defined within the proposed architecture, these filters are unable to efficiently encode the information about the object. Thus, we introduce a different technique based on anisotropic filters that adapt their shape and size according to the omnidirectional image geometry. The main advantage of this approach compared to the previous one is the ability to encode the orientation of an image pattern, which is important for various tasks such as image compression. Our experiments show that our approach adapts to different image projective geometries and achieves state-of-the-art performance on image classification and compression tasks. Overall we propose several methods, which combine the power of DL and graph signal processing towards incorporating prior information about the target task inside the optimization procedure. We hope that the research efforts presented in this thesis will help the development of efficient DL algorithms that can use various types of prior knowledge to make them efficient even when the available training data is scarce.

EPFL2019