NDLI: Characterizing prediction error distributions for lossless image compression

Content Provider	IEEE Xplore Digital Library
Author	Langdon Jr., G.G. Zandi, A.
Copyright Year	1996
Description	Author affiliation: Baskin Center of Comput. Eng., California Univ., Santa Cruz, CA, USA (Langdon, G.G., Jr.)
Abstract	In predictive coding for lossless image compression, full knowledge of the prediction error distribution and efficient coding with an arithmetic coding method is the best one can do with the 0-order model assumption. The zero-order error distributions typically are Laplacian with zero mean. Higher-order error distributions are often skewed with a mean that is often positive or negative. Additional compression is achieved by an accurate characterization of context-dependent error distributions. This paper presents the results of a study the different characteristics of the error distributions found in higher-order conditioning contexts of the LOCO and CALIC algorithms. The study includes nonstationary behavior.
Starting Page	573
Ending Page	576
File Size	354342
Page Count	4
File Format	PDF
ISBN	0818676469
ISSN	10586393
DOI	10.1109/ACSSC.1996.601087
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image coding Computer errors Predictive coding Predictive models Laplace equations Sun Solid modeling Distributed computing Knowledge engineering Digital arithmetic
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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