Good probabilistic models are needed in data compression and many other applications. A good model must exploit contextual information, which requires high-order conditioning. As the number of conditioning variables increases, direct estimation of the distribution becomes exponentially more difficult. To circumvent this, we consider a means of adaptively combining several low-order conditional probability distributions into a single higher-order estimate, based on their degree of agreement. Though the technique is broadly applicable, image compression is singled out as a testing ground of its abilities. Good performance is demonstrated by experimental results.<>
Exaggerated consensus in lossless image compression
Proceedings of 1st International Conference on Image Processing ; 3 ; 846-850 vol.3
1994-01-01
545598 byte
Conference paper
Electronic Resource
English
Exaggerated Consensus in Lossless Image Compression
British Library Conference Proceedings | 1994
|Fast lossless image compression
IEEE | 1996
|Fast Lossless Image Compression
British Library Conference Proceedings | 1996
|Near-lossless image compression techniques
British Library Online Contents | 1998
|Context-based lossless halftone image compression
British Library Online Contents | 1999
|