Side-information generation for temporally and spatially scalablewyner-ziv codecs

The distributed video coding paradigmenables video codecs to operate with reversed complexity, in which the complexity is shifted from the encoder toward the decoder. Its performance is heavily dependent on the quality of the side information generated by motio estimation at the decoder. We compare the rate-distortion performance of different side-information estimators, for both temporally and spatially scalableWyner-Ziv codecs. For the temporally scalable codec we compared an established method with a new algorithm that uses a linear-motion model to produce side-information. As a continuation of previous works, in this paper, we propose to use a super-resolution method to upsample the nonkey frame, for the spatial scalable codec, using the key frames as reference.We verify the performance of the spatial scalableWZcoding using the state-of-the-art video coding standard H.264/AVC

GReEn: a tool for efficient compression of genome resequencing data

Research in the genomic sciences is confronted with the volume of sequencing and resequencing data increasing at a higher pace than that of data storage and communication resources, shifting a significant part of research budgets from the sequencing component of a project to the computational one. Hence, being able to efficiently store sequencing and resequencing data is a problem of paramount importance. In this article, we describe GReEn (Genome Resequencing Encoding), a tool for compressing genome resequencing data using a reference genome sequence. It overcomes some drawbacks of the recently proposed tool GRS, namely, the possibility of compressing sequences that cannot be handled by GRS, faster running times and compression gains of over 100-fold for some sequences. This tool is freely available for non-commercial use at ftp://ftp.ieeta.pt/∼ap/codecs/GReEn1.tar.gz

Improved Sequential MAP estimation of CABAC encoded data with objective adjustment of the complexity/efficiency tradeoff

International audienceThis paper presents an efficient MAP estimator for the joint source-channel decoding of data encoded with a context adaptive binary arithmetic coder (CABAC). The decoding process is compatible with realistic implementations of CABAC in standards like H.264, i.e., handling adaptive probabilities, context modeling and integer arithmetic coding. Soft decoding is obtained using an improved sequential decoding technique, which allows to obtain various tradeoffs between complexity and efficiency. The algorithms are simulated in a context reminiscent of H264. Error detection is realized by exploiting on one side the properties of the binarization scheme and on the other side the redundancy left in the code string. As a result, the CABAC compression efficiency is preserved and no additional redundancy is introduced in the bit stream. Simulation results outline the efficiency of the proposed techniques for encoded data sent over AWGN and UMTS-OFDM channels

Compressing Proteomes: The Relevance of Medium Range Correlations

We study the nonrandomness of proteome sequences by analysing the correlations that arise between amino acids at a short and medium range, more specifically, between amino acids located 10 or 100 residues apart; respectively. We show that statistical models that consider these two types of correlation are more likely to seize the information contained in protein sequences and thus achieve good compression rates. Finally, we propose that the cause for this redundancy is related to the evolutionary origin of proteomes and protein sequences

Using semantic knowledge to improve compression on log files

With the move towards global and multi-national companies, information technology infrastructure requirements are increasing. As the size of these computer networks increases, it becomes more and more difficult to monitor, control, and secure them. Networks consist of a number of diverse devices, sensors, and gateways which are often spread over large geographical areas. Each of these devices produce log files which need to be analysed and monitored to provide network security and satisfy regulations. Data compression programs such as gzip and bzip2 are commonly used to reduce the quantity of data for archival purposes after the log files have been rotated. However, there are many other compression programs which exist - each with their own advantages and disadvantages. These programs each use a different amount of memory and take different compression and decompression times to achieve different compression ratios. System log files also contain redundancy which is not necessarily exploited by standard compression programs. Log messages usually use a similar format with a defined syntax. In the log files, all the ASCII characters are not used and the messages contain certain "phrases" which often repeated. This thesis investigates the use of compression as a means of data reduction and how the use of semantic knowledge can improve data compression (also applying results to different scenarios that can occur in a distributed computing environment). It presents the results of a series of tests performed on different log files. It also examines the semantic knowledge which exists in maillog files and how it can be exploited to improve the compression results. The results from a series of text preprocessors which exploit this knowledge are presented and evaluated. These preprocessors include: one which replaces the timestamps and IP addresses with their binary equivalents and one which replaces words from a dictionary with unused ASCII characters. In this thesis, data compression is shown to be an effective method of data reduction producing up to 98 percent reduction in filesize on a corpus of log files. The use of preprocessors which exploit semantic knowledge results in up to 56 percent improvement in overall compression time and up to 32 percent reduction in compressed size.TeXpdfTeX-1.40.

Analytical tools for optimizing the error correction performance of arithmetic codes

International audienceIn joint source-channel arithmetic coding (JSCAC) schemes, additional redundancy may be introduced into an arithmetic source code in order to be more robust against transmission errors. The purpose of this work is to provide analytical tools to predict and evaluate the effectiveness of that redundancy. Integer binary Arithmetic Coding (AC) is modeled by a reduced-state automaton in order to obtain a bit-clock trellis describing the encoding process. Considering AC as a trellis code, distance spectra are then derived. In particular, an algorithm to compute the free distance of an arithmetic code is proposed. The obtained code properties allow to compute upper bounds on both bit error and symbol error probabilities and thus provide an objective criterion to analyze the behavior of JSCAC schemes when used on noisy channels. This criterion is then exploited to design efficient error-correcting arithmetic codes. Simulation results highlight the validity of the theoretical error bounds and show that for equivalent rate and complexity, a simple optimization yields JSCACs that outperform classical tandem schemes at low to medium SNR