Source coding and lossless data compression

International audience Analytic information theory aims at studying problems of information theory using analytic techniques of computer science and combinatorics. Following Hadamard's precept, these problems are tackled by complex analysis methods such as generating functions, Mellin transform, Fourier series, saddle point method, analytic poissonization and depoissonization, and singularity analysis. This approach lies at the crossroad of computer science and information theory. In this survey we concentrate on one facet of information theory (i.e., source coding better known as data compression), namely the $\textit{redundancy rate}$ problem. The redundancy rate problem determines by how much the actual code length exceeds the optimal code length. We further restrict our interest to the $\textit{average}$ redundancy for $\textit{known}$ sources, that is, when statistics of information sources are known. We present precise analyses of three types of lossless data compression schemes, namely fixed-to-variable (FV) length codes, variable-to-fixed (VF) length codes, and variable-to-variable (VV) length codes. In particular, we investigate average redundancy of Huffman, Tunstall, and Khodak codes. These codes have succinct representations as $\textit{trees}$, either as coding or parsing trees, and we analyze here some of their parameters (e.g., the average path from the root to a leaf).

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Fundamental Limits of Lossless Data Compression with Side Information

IEEE Transactions on Information Theory ◽

10.1109/tit.2021.3062614 ◽

2021 ◽

pp. 1-1

Author(s):

Lampros Gavalakis ◽

Ioannis Kontoyiannis

Keyword(s):

Data Compression ◽

Side Information ◽

Fundamental Limits ◽

Lossless Data Compression

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FPGA-Based Lossless Data Compression using Huffman and LZ77 Algorithms

2007 Canadian Conference on Electrical and Computer Engineering ◽

10.1109/ccece.2007.315 ◽

2007 ◽

Cited By ~ 38

Author(s):

Suzanne Rigler ◽

William Bishop ◽

Andrew Kennings

Keyword(s):

Data Compression ◽

Lossless Data Compression

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Design and implementation of data logger using lossless data compression method for Internet of Things

2016 International Conference on Frontiers of Information Technology (FIT) ◽

10.1109/fit.2016.7857547 ◽

2016 ◽

Author(s):

Febrian Hadiatna ◽

Hilwadi Hindersah ◽

Desta Yolanda ◽

Muhammad Agus Triawan

Keyword(s):

Internet Of Things ◽

Data Compression ◽

Compression Method ◽

Data Logger ◽

Design And Implementation ◽

Lossless Data Compression

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Joint Source-Channel Coding in Dictionary Methods of Lossless Data Compression

International Journal of Electronics and Telecommunications ◽

10.2478/v10177-010-0046-8 ◽

2010 ◽

Vol 56 (4) ◽

pp. 351-355

Author(s):

Marcin Rodziewicz

Keyword(s):

Data Compression ◽

Channel Coding ◽

Data Stream ◽

Noisy Channels ◽

Error Resiliency ◽

Joint Source Channel Coding ◽

Lossless Data Compression ◽

Compressed Data ◽

Source Channel Coding

Joint Source-Channel Coding in Dictionary Methods of Lossless Data Compression Limitations on memory and resources of communications systems require powerful data compression methods. Decompression of compressed data stream is very sensitive to errors which arise during transmission over noisy channels, therefore error correction coding is also required. One of the solutions to this problem is the application of joint source and channel coding. This paper contains a description of methods of joint source-channel coding based on the popular data compression algorithms LZ'77 and LZSS. These methods are capable of introducing some error resiliency into compressed stream of data without degradation of the compression ratio. We analyze joint source and channel coding algorithms based on these compression methods and present their novel extensions. We also present some simulation results showing usefulness and achievable quality of the analyzed algorithms.

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