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1997
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Minimum-Entropy Clustering and its Application to Lossless Image Coding

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Minimum-Entropy Clustering and its Application to Lossless Image Coding
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 xed number of contiguous samples from a discrete source) using a minimum entropy criterion. In a manner similar to Classi ed Vector Quantization (CVQ), a given vector is rst classi ed into the class which leads to the lowest entropy and then its samples are coded by the entropy coder designed for that particular class. In this paper the MEC algorithm is used in the design of a lossless, predictive image coder. The MEC-based coder is found to sigi cantly outperform the single entropy coder as well as the other popular lossless coders reported in the literature.
Farshid Golchin, Kuldip K. Paliwal
Added 26 Oct 2009
Updated 26 Oct 2009
Type Conference
Year 1997
Where ICIP
Authors Farshid Golchin, Kuldip K. Paliwal
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