Segmentation of Source Symbols for Adaptive Arithmetic Coding

Adaptive arithmetic coding is a general technique for coding source symbols of a stochastic process based on an adaptive model. The adaptive model provides measures of the statistics of source symbols and is updated, along with encoding/decoding processes, when more encoded/decoded symbols are fed a...

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Vydané v:	IEEE transactions on broadcasting Ročník 58; číslo 2; s. 228 - 235
Hlavní autori:	Zhang, Liang, Wang, Demin, Zheng, Dong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.06.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Adaptive coding Applied sciences Arithmetic Coders Coding Coding, codes Encoding Entropy Exact sciences and technology Image processing Information, signal and communications theory predictive coding Redundancy Samples Signal and communications theory Signal processing Signal representation. Spectral analysis signal representations Signal, noise Spatial resolution Statistical analysis Statistical methods Statistics Studies Symbols Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Transmission and modulation (techniques and equipments) vector quantization Vectors video coding video compression Video sequences Video coding Performance evaluation Lossless circuit Image processing Segmentation Data compression Video signal Updating Stochastic process Adaptive method Video signal processing Image coding signal representations Signal transmission Motion estimation Vector quantization Coding circuit Image compression Decoding Signal representation Algorithm Adaptive coding video compression Predictive coding Arithmetic code Signal processing
ISSN:	0018-9316, 1557-9611
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Popis
Shrnutí:	Adaptive arithmetic coding is a general technique for coding source symbols of a stochastic process based on an adaptive model. The adaptive model provides measures of the statistics of source symbols and is updated, along with encoding/decoding processes, when more encoded/decoded symbols are fed as samples to the adaptive model. The coding performance depends on how well the adaptive model fits the statistics of source symbols. If the number of source symbols is large and the number of samples is small, the adaptive model may not be able to provide valid measures of the statistics, which results in an inefficient coding performance of the adaptive arithmetic coder. To this end, this paper presents segmentation of source symbols to improve the performance of the adaptive arithmetic coder. Each source symbol is divided into several segments. Each segment is separately coded with an adaptive arithmetic coder. With this division, possible values of each segment are concentrated within a small range. Given the limited number of samples, this concentration leads to a better fit of the adaptive model to the statistics of source symbols and therefore to an improvement of the coding efficiency. The proposed coding algorithm is applied to lossless motion vector coding for video transmission as an application example to show its performance improvement and coding gains.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	0018-9316 1557-9611
DOI:	10.1109/TBC.2012.2186728