DocumentCode :
35422
Title :
Bayesian Predictor Combination for Lossless Image Compression
Author :
Martchenko, Andrew ; Guang Deng
Author_Institution :
Dept. of Electron. Eng., La Trobe Univ., Melbourne, VIC, Australia
Volume :
22
Issue :
12
fYear :
2013
fDate :
Dec. 2013
Firstpage :
5263
Lastpage :
5270
Abstract :
Adaptive predictor combination (APC) is a framework for combining multiple predictors for lossless image compression and is often at the core of state-of-the-art algorithms. In this paper, a Bayesian parameter estimation scheme is proposed for APC. Extensive experiments using natural, medical, and remote sensing images of 8-16 bit/pixel have confirmed that the predictive performance is consistently better than that of APC for any combination of fixed predictors and with only a marginal increase in computational complexity. The predictive performance improves with every additional fixed predictor, a property that is not found in other predictor combination schemes studied in this paper. Analysis and simulation show that the performance of the proposed algorithm is not sensitive to the choice of hyper-parameters of the prior distributions. Furthermore, the proposed prediction scheme provides a theoretical justification for the error correction stage that is often included as part of a prediction process.
Keywords :
Bayes methods; data compression; image coding; maximum likelihood estimation; prediction theory; Bayesian parameter estimation scheme; Bayesian predictor combination; adaptive predictor combination; computational complexity; error correction stage; lossless image compression; medical images; natural images; predictive performance; remote sensing images; Bayes methods; Complexity theory; Entropy; Entropy coding; Image coding; Maximum likelihood estimation; Prediction algorithms; Bayesian learning; Lossless image compression; adaptive prediction; context modeling; entropy coding;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2013.2284067
Filename :
6616680
Link To Document :
بازگشت