NDLI: Overlap in adaptive vector quantization

Content Provider	IEEE Xplore Digital Library
Author	Rizzo, F. Storer, J.A.
Copyright Year	2001
Description	Author affiliation: Dept. of Comput. Sci., Brandeis Univ., Waltham, MA, USA (Rizzo, F.)
Abstract	Constantinescu and Storer (1994) introduced an adaptive single-pass vector quantization algorithm (AVQ) that employs variable size and shaped codebook entries that are "learned" as an image is processed (no specific training or prior knowledge of the data is used). The approach allows the tradeoff between compression and fidelity to be continuously adjusted from lossless (with less compression) to highly lossy (with greater compression). Although practical performance compares favorably with the JPEG standard as well as standard trained vector quantization implementations, analysis of its performance appears difficult. A key aspect of AVQ is that matches are allowed to overlap, and it is not necessary to perform some sort of bin packing in order to cover the image with variable size and shape matches. Here we show that the AVQ approach is in some sense optimal asymptotically, module the overlapping factor which is defined to be the average number of times that a pixel is covered. We also present experiments that study the relationship of overlapping to performance.
Sponsorship	Brandeis Univ.
Starting Page	401
Ending Page	410
File Size	717894
Page Count	10
File Format	PDF
ISBN	0769510310
ISSN	10680314
DOI	10.1109/DCC.2001.917171
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-03-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vector quantization Dictionaries Image coding Computer science Performance analysis Shape Transform coding Entropy coding Performance loss Data compression
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications

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