Arithmetic bit recycling data compression

La compression des données est la technique informatique qui vise à réduire la taille de l’information pour minimiser l’espace de stockage nécessaire et accélérer la transmission des données dans les réseaux à bande passante limitée. Plusieurs techniques de compression telles que LZ77 et ses variantes souffrent d’un problème que nous appelons la redondance causée par la multiplicité d’encodages. La multiplicité d’encodages (ME) signifie que les données sources peuvent être encodées de différentes manières. Dans son cas le plus simple, ME se produit lorsqu’une technique de compression a la possibilité, au cours du processus d’encodage, de coder un symbole de différentes manières. La technique de compression par recyclage de bits a été introduite par D. Dubé et V. Beaudoin pour minimiser la redondance causée par ME. Des variantes de recyclage de bits ont été appliquées à LZ77 et les résultats expérimentaux obtenus conduisent à une meilleure compression (une réduction d’environ 9% de la taille des fichiers qui ont été compressés par Gzip en exploitant ME). Dubé et Beaudoin ont souligné que leur technique pourrait ne pas minimiser parfaitement la redondance causée par ME, car elle est construite sur la base du codage de Huffman qui n’a pas la capacité de traiter des mots de code (codewords) de longueurs fractionnaires, c’est-à-dire qu’elle permet de générer des mots de code de longueurs intégrales. En outre, le recyclage de bits s’appuie sur le codage de Huffman (HuBR) qui impose des contraintes supplémentaires pour éviter certaines situations qui diminuent sa performance. Contrairement aux codes de Huffman, le codage arithmétique (AC) peut manipuler des mots de code de longueurs fractionnaires. De plus, durant ces dernières décennies, les codes arithmétiques ont attiré plusieurs chercheurs vu qu’ils sont plus puissants et plus souples que les codes de Huffman. Par conséquent, ce travail vise à adapter le recyclage des bits pour les codes arithmétiques afin d’améliorer l’efficacité du codage et sa flexibilité. Nous avons abordé ce problème à travers nos quatre contributions (publiées). Ces contributions sont présentées dans cette thèse et peuvent être résumées comme suit. Premièrement, nous proposons une nouvelle technique utilisée pour adapter le recyclage de bits qui s’appuie sur les codes de Huffman (HuBR) au codage arithmétique. Cette technique est nommée recyclage de bits basé sur les codes arithmétiques (ACBR). Elle décrit le cadriciel et les principes de l’adaptation du HuBR à l’ACBR. Nous présentons aussi l’analyse théorique nécessaire pour estimer la redondance qui peut être réduite à l’aide de HuBR et ACBR pour les applications qui souffrent de ME. Cette analyse démontre que ACBR réalise un recyclage parfait dans tous les cas, tandis que HuBR ne réalise de telles performances que dans des cas très spécifiques. Deuxièmement, le problème de la technique ACBR précitée, c’est qu’elle requiert des calculs à précision arbitraire. Cela nécessite des ressources illimitées (ou infinies). Afin de bénéficier de cette dernière, nous proposons une nouvelle version à précision finie. Ladite technique devienne ainsi efficace et applicable sur les ordinateurs avec les registres classiques de taille fixe et peut être facilement interfacée avec les applications qui souffrent de ME. Troisièmement, nous proposons l’utilisation de HuBR et ACBR comme un moyen pour réduire la redondance afin d’obtenir un code binaire variable à fixe. Nous avons prouvé théoriquement et expérimentalement que les deux techniques permettent d’obtenir une amélioration significative (moins de redondance). À cet égard, ACBR surpasse HuBR et fournit une classe plus étendue des sources binaires qui pouvant bénéficier d’un dictionnaire pluriellement analysable. En outre, nous montrons qu’ACBR est plus souple que HuBR dans la pratique. Quatrièmement, nous utilisons HuBR pour réduire la redondance des codes équilibrés générés par l’algorithme de Knuth. Afin de comparer les performances de HuBR et ACBR, les résultats théoriques correspondants de HuBR et d’ACBR sont présentés. Les résultats montrent que les deux techniques réalisent presque la même réduction de redondance sur les codes équilibrés générés par l’algorithme de Knuth.

Quadtree representation and compression of spatial data

Spatial data representation and compression has become a focus issue in computer graphics and image processing applications. Quadtrees, as one of hierarchical data structures, basing on the principle of recursive decomposition of space, always offer a compact and efficient representation of an image. For a given image, the choice of quadtree root node plays an important role in its quadtree representation and final data compression. The goal of this thesis is to present a heuristic algorithm for finding a root node of a region quadtree, which is able to reduce the number of leaf nodes when compared with the standard quadtree decomposition. The empirical results indicate that, this proposed algorithm has quadtree representation and data compression improvement when in comparison with the traditional method.

An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.

A firmware data compression unit /

Originally presented as the author's thesis (M.S.), University of Illinois at Urbana-Champaign.

Data compression for character strings /

Mode of access: Internet.

Applications of fractals to image data compression

Digital image processing is exploited in many diverse applications but the size of digital images places excessive demands on current storage and transmission technology. Image data compression is required to permit further use of digital image processing. Conventional image compression techniques based on statistical analysis have reached a saturation level so it is necessary to explore more radical methods. This thesis is concerned with novel methods, based on the use of fractals, for achieving significant compression of image data within reasonable processing time without introducing excessive distortion. Images are modelled as fractal data and this model is exploited directly by compression schemes. The validity of this is demonstrated by showing that the fractal complexity measure of fractal dimension is an excellent predictor of image compressibility. A method of fractal waveform coding is developed which has low computational demands and performs better than conventional waveform coding methods such as PCM and DPCM. Fractal techniques based on the use of space-filling curves are developed as a mechanism for hierarchical application of conventional techniques. Two particular applications are highlighted: the re-ordering of data during image scanning and the mapping of multi-dimensional data to one dimension. It is shown that there are many possible space-filling curves which may be used to scan images and that selection of an optimum curve leads to significantly improved data compression. The multi-dimensional mapping property of space-filling curves is used to speed up substantially the lookup process in vector quantisation. Iterated function systems are compared with vector quantisers and the computational complexity or iterated function system encoding is also reduced by using the efficient matching algcnithms identified for vector quantisers.

Data transmission by pulse compression

Pulse compression techniques originated in radar.The present work is concerned with the utilization of these techniques in general, and the linear FM (LFM) technique in particular, for comnunications. It introduces these techniques from an optimum communications viewpoint and outlines their capabilities.It also considers the candidacy of the class of LFM signals for digital data transmission and the LFM spectrum. Work related to the utilization of LFM signals for digital data transmission has been mostly experimental and mainly concerned with employing two rectangular LFM pulses (or chirps) with reversed slopes to convey the bits 1 and 0 in an incoherent node.No systematic theory for LFM signal design and system performance has been available. Accordingly, the present work establishes such a theory taking into account coherent and noncoherent single-link and multiplex signalling modes. Some new results concerning the slope-reversal chirp pair are obtained. The LFM technique combines the typical capabilities of pulse compression with a relative ease of implementation. However, these merits are often hampered by the difficulty of handling the LFM spectrum which cannot generally be expressed closed-form. The common practice is to obtain a plot of this spectrum with a digital computer for every single set of LFM pulse parameters.Moreover, reported work has been Justly confined to the spectrum of an ideally rectangular chirp pulse with no rise or fall times.Accordingly, the present work comprises a systerratic study of the LFM spectrum which takes the rise and fall time of the chirp pulse into account and can accommodate any LFM pulse with any parameters.It· formulates rather simple and accurate prediction criteria concerning the behaviour of this spectrum in the different frequency regions. These criteria would facilitate the handling of the LFM technique in theory and practice.

Forming of Learning Set for Neural Networks in Problems of Losless Data Compression

questions of forming of learning sets for artificial neural networks in problems of lossless data compression are considered. Methods of construction and use of learning sets are studied. The way of forming of learning set during training an artificial neural network on the data stream is offered.

A novel lossless compression technique for text data

The focus of this thesis is placed on text data compression based on the fundamental coding scheme referred to as the American Standard Code for Information Interchange or ASCII. The research objective is the development of software algorithms that result in significant compression of text data. Past and current compression techniques have been thoroughly reviewed to ensure proper contrast between the compression results of the proposed technique with those of existing ones. The research problem is based on the need to achieve higher compression of text files in order to save valuable memory space and increase the transmission rate of these text files. It was deemed necessary that the compression algorithm to be developed would have to be effective even for small files and be able to contend with uncommon words as they are dynamically included in the dictionary once they are encountered. A critical design aspect of this compression technique is its compatibility to existing compression techniques. In other words, the developed algorithm can be used in conjunction with existing techniques to yield even higher compression ratios. This thesis demonstrates such capabilities and such outcomes, and the research objective of achieving higher compression ratio is attained.

Content-aware compression for big textual data analysis

A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.