Title :
Minimum-entropy clustering and its application to lossless image coding
Author :
Golchin, Farshid ; Paliwal, Kuldip K.
Author_Institution :
Sch. of Microelectron. Eng., Griffith Univ., Brisbane, Qld., Australia
Abstract :
The minimum-entropy clustering (MEC) algorithm proposed in this paper provides an optimal method for addressing the non-stationarity of a source with respect to entropy coding. This algorithm clusters a set of vectors (where each vector consists of a fixed number of contiguous samples from a discrete source) using a minimum entropy criterion. In a manner similar to classified vector quantization (CVQ), a given vector is first classified into the class which leads to the lowest entropy and then its samples are coded by the entropy coder designed for that particular class. The MEC algorithm is used in the design of a lossless, predictive image coder. The MEC-based coder is found to significantly outperform the single entropy coder as well as the other popular lossless coders reported in the literature
Keywords :
adaptive codes; correlation methods; differential pulse code modulation; entropy codes; image classification; image coding; image recognition; minimum entropy methods; prediction theory; vector quantisation; MEC algorithm; classified vector quantization; correlated DPCM image; discrete source; entropy coding; lossless image coding; minimum entropy criterion; minimum-entropy clustering; predictive image coder; samples; Algorithm design and analysis; Astronomy; Australia; Biomedical imaging; Clustering algorithms; Entropy coding; Image coding; Medical diagnosis; Microelectronics; Performance loss; Signal resolution; Vector quantization;
Conference_Titel :
Image Processing, 1997. Proceedings., International Conference on
Conference_Location :
Santa Barbara, CA
Print_ISBN :
0-8186-8183-7
DOI :
10.1109/ICIP.1997.638738
