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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Veröffentlicht in:IEEE transactions on broadcasting Jg. 58; H. 2; S. 228 - 235
Hauptverfasser: Zhang, Liang, Wang, Demin, Zheng, Dong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.06.2012
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Beschreibung
Zusammenfassung: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.
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ISSN:0018-9316
1557-9611
DOI:10.1109/TBC.2012.2186728