Integer Set Compression and Statistical Modeling

Compression of integer sets and sequences has been extensively studied for settings where elements follow a uniform probability distribution. In addition, methods exist that exploit clustering of elements in order to achieve higher compression performance. In this work, we address the case where enumeration of elements may be arbitrary or random, but where statistics is kept in order to estimate probabilities of elements. We present a recursive subset-size encoding method that is able to benefit from statistics, explore the effects of permuting the enumeration order based on element probabilities, and discuss general properties and possibilities for this class of compression problem

Комбинированное кодирование битовых плоскостей изображений

The aim of this work is to reduce the computational complexity of lossless compression in the spatial domain due to the combined coding (arithmetic and Run-Length Encoding) of a series of bits of bit planes. Known effective compression encoders separately encode the bit planes of the image or transform coefficients, which leads to an increase in computational complexity due to multiple processing of each pixel. The paper proposes the rules for combined coding and combined encoders for bit planes of pixel differences of images with a tunable and constant structure, which have lower computational complexity and the same compression ratio as compared to an arithmetic encoder of bit planes.Целью работы является снижение вычислительной сложности сжатия полутоновых изображений без потерь в пространственной области за счет комбинированного кодирования арифметического и длин серий бит битовых плоскостей. Известные эффективные кодеры сжатия раздельно кодируют битовые плоскости изображения или коэффициентов преобразования, что приводит к росту вычислительной сложности из-за многократной обработки каждого пикселя. В работе предложены правила комбинированного кодирования и комбинированные кодеры битовых плоскостей разностей пикселей изображений с перестраиваемой и постоянной структурой, имеющие по сравнению с арифметическим кодером битовых плоскостей меньшую вычислительную сложность и такой же коэффициент сжатия

Analysis of Arithmetic Coding for Data Compression

Arithmetic coding, in conjunction with a suitable probabilistic model, can pro- vide nearly optimal data compression. In this article we analyze the e ect that the model and the particular implementation of arithmetic coding have on the code length obtained. Periodic scaling is often used in arithmetic coding im- plementations to reduce time and storage requirements; it also introduces a recency e ect which can further a ect compression. Our main contribution is introducing the concept of weighted entropy and using it to characterize in an elegant way the e ect that periodic scaling has on the code length. We explain why and by how much scaling increases the code length for les with a ho- mogeneous distribution of symbols, and we characterize the reduction in code length due to scaling for les exhibiting locality of reference. We also give a rigorous proof that the coding e ects of rounding scaled weights, using integer arithmetic, and encoding end-of- le are negligible

Arithmetic coding revisited

Over the last decade, arithmetic coding has emerged as an important compression tool. It is now the method of choice for adaptive coding on multisymbol alphabets because of its speed, low storage requirements, and effectiveness of compression. This article describes a new implementation of arithmetic coding that incorporates several improvements over a widely used earlier version by Witten, Neal, and Cleary, which has become a de facto standard. These improvements include fewer multiplicative operations, greatly extended range of alphabet sizes and symbol probabilities, and the use of low-precision arithmetic, permitting implementation by fast shift/add operations. We also describe a modular structure that separates the coding, modeling, and probability estimation components of a compression system. To motivate the improved coder, we consider the needs of a word-based text compression program. We report a range of experimental results using this and other models. Complete source code is available

Word-based compression in full-text retrieval systems

Ankara : Department of Industrial Engineering and the Institute of Engineering and Sciences of Bilkent University, 1995.Thesis (Master's) -- Bilkent University, 1995.Includes bibliographical references leaves 44-49.Large space requirement of a full-text retrieval system can be reduced significantly by data compression. In this study, the problem of compressing the main text of a full-text retrieval system is addressed and performance of several coding techniques for compressing the text database is compared. Experiments show that statistical techniques, such as arithmetic coding and Huffman coding, give the best compression among the implemented; and using a semi-static word-based model, the space needed to store English text is less than one third of the original requirement.Selçuk, Ali AydınM.S

Comparison of Methods for Text Compression

One of the purposes of this research was to introduce several well-known text compression methods and to test them in order to compare their performances, not only with each other, but also with results from various previous researches. Welch's implementation of the Ziv-Lempel method was found to outperform any other single method introduced in this thesis, or any combination of methods. One other purpose of this research was to calculate the average distance from any one bit to the next synchronization point in static Huffman decoding, following a decoding error. The average distance in words decoded was predicted to be the average length of a codeword in bits, and tests on resynchronization showed that this was a good prediction.Computing and Information Scienc

Some new developments in image compression

This study is divided into two parts. The first part involves an investigation of near-lossless compression of digitized images using the entropy-coded DPCM method with a large number of quantization levels. Through the investigation, a new scheme that combines both lossy and lossless DPCM methods into a common framework is developed. This new scheme uses known results on the design of predictors and quantizers that incorporate properties of human visual perception. In order to enhance the compression performance of the scheme, an adaptively generated source model with multiple contexts is employed for the coding of the quantized prediction errors, rather than a memoryless model as in the conventional DPCM method. Experiments show that the scheme can provide compression in the range from 4 to 11 with a peak SNR of about 50 dB for 8-bit medical images. Also, the use of multiple contexts is found to improve compression performance by about 25% to 35%;The second part of the study is devoted to the problem of lossy image compression using tree-structured vector quantization. As a result of the study, a new design method for codebook generation is developed together with four different implementation algorithms. In the new method, an unbalanced tree-structured vector codebook is designed in a greedy fashion under the constraint of rate-distortion trade-off which can then be used to implement a variable-rate compression system. From experiments, it is found that the new method can achieve a very good rate-distortion performance while being computationally efficient. Also, due to the tree-structure of the codebook, the new method is amenable to progressive transmission applications

Universal homophonic coding

Redundancy in plaintext is a fertile source of attack in any encryption system. Compression before encryption reduces the redundancy in the plaintext, but this does not make a cipher more secure. The cipher text is still susceptible to known-plaintext and chosen-plaintext attacks. The aim of homophonic coding is to convert a plaintext source into a random sequence by randomly mapping each source symbol into one of a set of homophones. Each homophone is then encoded by a source coder after which it can be encrypted with a cryptographic system. The security of homophonic coding falls into the class of unconditionally secure ciphers. The main advantage of homophonic coding over pure source coding is that it provides security both against known-plaintext and chosen-plaintext attacks, whereas source coding merely protects against a ciphertext-only attack. The aim of this dissertation is to investigate the implementation of an adaptive homophonic coder based on an arithmetic coder. This type of homophonic coding is termed universal, as it is not dependent on the source statistics.Computer ScienceM.Sc. (Computer Science