Conference
Hierarchical Modeling via Optimal Context Quantization
Abstract
Optimal context quantization with respect to the minimum conditional entropy (MCECQ) is proven to be an efficient way for high order statistical modeling and model complexity reduction in data compression systems. The MCECQ merges together contexts that have similar statistics to reduce the size of the original model. In this technique, the number of output clusters (the model size) must be set before quantization. Optimal model size for the …
Authors
Krivoulets A; Wu X
Pagination
pp. 380-384
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publication Date
January 1, 2003
DOI
10.1109/iciap.2003.1234079
Name of conference
12th International Conference on Image Analysis and Processing, 2003.Proceedings.