Journal article
Optimal context quantization in lossless compression of image data sequences
In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image.
We consider the problem of finding the optimal quantizer Q that quantizes the K-dimensional causal context C/sub t/=(X/sub t-t1/,X/sub t-t2/,...,X/sub t-tK/) of a source symbol X/sub t/ into one of a set of conditioning states. The optimality of context quantization is defined to be the minimum static or minimum adaptive code length of given a data set.
For a binary source alphabet an optimal context quantizer can be computed exactly by a fast dynamic programming algorithm. Faster approximation solutions are also proposed. In case of m-ary source alphabet a random variable can be decomposed into a sequence of binary decisions, each of which is coded using optimal context quantization designed for the corresponding binary random variable.
This optimized coding scheme is applied to digital maps and /spl alpha-/plane sequences. The proposed optimal context quantization technique can also be used to establish a lower bound on the achievable code length, and hence is a useful tool to evaluate the performance of existing heuristic context quantizers.
Language: | English |
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Publisher: | IEEE |
Year: | 2004 |
Pages: | 509-517 |
ISSN: | 19410042 and 10577149 |
Types: | Journal article |
DOI: | 10.1109/TIP.2003.822613 |
ORCIDs: | Forchhammer, Søren and Andersen, Jakob Dahl |
/spl alpha/-plane sequences Adaptive coding Algorithms Computer Simulation Context modeling Data Compression Dynamic programming Entropy coding Hypermedia Image Enhancement Image Interpretation, Computer-Assisted Image coding Image resolution K-dimensional causal context Models, Statistical Pattern Recognition, Automated Pixel Quality Control Quantization Random variables Reproducibility of Results Sample Size Sensitivity and Specificity Signal Processing, Computer-Assisted Statistics Subtraction Technique Video Recording adaptive codes binary source alphabet context dilution context-based entropy coding data compression digital maps dynamic programming entropy codes high-order statistics higher order statistics image coding image compression image data sequences image sequences lossless compression m-ary source alphabet minimum adaptive code length minimum static code length optimal context quantization