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Tree-based search for ECVQ

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PPM*-style context sorting compression method using a prefix list

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Semantic progressive transmission for deep space communications

Protection of regions of interest against data loss in a generalized multiple description framework

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Data compression with truncated suffix trees

Decoding of canonical Huffman codes with look-up tables

Coding of image residuals with tailbiting convolutional codes and BCJR decoding

High performance Lempel-Ziv compression using optimized longest string parsing and adaptive Huffman window size

Wireless image transmission using multiple-description based concatenated codes

Effect of image activity on lossy and lossless coding performance

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Compression of SMIL documents

Variable-to-fixed length codes: a geometrical approach to low-complexity source codes

Lossless compression of high-volume numerical data from simulations

Exploiting interframe redundancies in the lossless compression of 3D medical images

RD-optimization of hierarchical structured adaptive vector quantization for video coding

A three-layer, two description image coder

Wavelet-based lossy compression of turbulence data

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Tree-based search for ECVQ

Content Provider	IEEE Xplore Digital Library
Author	Cardinal, J.
Copyright Year	2000
Description	Author affiliation: Univ. Libre de Bruxelles, Belgium (Cardinal, J.)
Abstract	Summary form only given. We propose two new tree-based search algorithms for vector quantizers using an additive weighted distance measure, such as ECVQ (entropy constrained vector quantization) (Chou et al., 1989). Both algorithms are based on a recursive space division technique, and use a bounding object at each node of the tree, in order to quickly eliminate subsets of the codebook during the search. The structure is more general than the k-d tree and the algorithm performs an optimal search similar to the one analyzed by Berchtold et al. (1997). We prove a theorem that defines the necessary and sufficient condition for any set of points to be a valid bounding object, i.e. to define a lossless pruning rule for the additive weighed Euclidean distance. The first algorithm presented uses rectangles as bounding objects, and the other uses spheres. We experimentally compare our approach with another recent one (Johnson et al., 1996), and show that the new algorithm using bounding rectangles performs significantly better for medium and high bitrate coding (>0.1 bits/sample) of a Gaussian process. This algorithm uses approximately 29 times less multiplications than a full codebook search at 1 bits/sample.
File Size	66493
File Format	PDF
ISBN	0769505929
ISSN	10680314
DOI	10.1109/DCC.2000.838195
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-03-29
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Additives Weight measurement Entropy Vector quantization Performance analysis Algorithm design and analysis Sufficient conditions Euclidean distance Bit rate Gaussian processes
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications

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