DocumentCode :
2705588
Title :
Prediction based on backward adaptive recognition of local texture orientation and Poisson statistical model for lossless/near-lossless image compression
Author :
Xue, Xiaohui ; Gao, Wen
Author_Institution :
Dept. of Comput. Sci., Harbin Inst. of Technol., China
Volume :
6
fYear :
1999
fDate :
15-19 Mar 1999
Firstpage :
3137
Abstract :
This paper is devoted to prediction-based lossless/near-lossless image compression algorithm. Within this framework, there are three modules, including prediction model, statistical model and entropy coding. This paper focuses on the former two, and puts forward two new methods: prediction model based on backward adaptive recognition of local texture orientation (BAROLTO), and Poisson statistical model. As far as we know, BAROLTO is the best predictor in efficiency. The Poisson model is designed to avoid the context dilution to some extent and make use of a large neighborhood; therefore, we can capture more local correlation. Experiments show that our compression system (BP) based on BAROLTO prediction and Poisson model outperforms the products of IBM and HP significantly
Keywords :
Poisson distribution; data compression; image coding; image recognition; image texture; prediction theory; BAROLTO; Poisson statistical model; backward adaptive recognition of local texture orientation; entropy coding; lossless image compression; near-lossless image compression; prediction model; prediction-based image compression; statistical model; Decorrelation; Entropy coding; Image coding; Image recognition; Power system modeling; Predictive models; Proposals; Robustness; Statistics; Transform coding;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
Conference_Location :
Phoenix, AZ
ISSN :
1520-6149
Print_ISBN :
0-7803-5041-3
Type :
conf
DOI :
10.1109/ICASSP.1999.757506
Filename :
757506
Link To Document :
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