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Error Modeling for Hierarchical Lossless Image Compression
Howard, Paul G. Vitter, Jeffrey Scott
Howard, Paul G.
Vitter, Jeffrey Scott
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Abstract
We present a new method for error modeling applicable to the MLP algorithm
for hierarchical lossless image compression. This method, based on a concept
called the variability index, provides accurate models for pixel prediction errors
without requiring explicit transmission of the models. We also use the vari-
ability index to show that prediction errors do not always follow the Laplace
distribution, as is commonly assumed; replacing the Laplace distribution with a
more general symmetric exponential distribution further improves compression.
We describe a new compression measurement called compression gain, and we
give experimental results showing that the MLP method using the variability
index technique for error modeling gives signi cantly more compression gain
than other methods in the literature.
Description
(c) 1992 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Date
1992
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IEEE
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Keywords
Data compression, Predictive image coding, Error modeling
Citation
P. G. Howard and J. S. Vitter. “Error Modeling for Hierarchical Lossless Image Compression,” Proceedings of the 1992 IEEE Data Compression Conference (DCC ’92), Snowbird, UT, March 1992, 269–278. http://dx.doi.org/10.1109/DCC.1992.227454