Data compression: methods and theory
Data compression: methods and theory
Text compression
Vector quantization and signal compression
Vector quantization and signal compression
Elements of information theory
Elements of information theory
Online adaptive vector quantization with variable size codebook entries
Information Processing and Management: an International Journal - Special issue: data compression
Error-Resilient Optimal Data Compression
SIAM Journal on Computing
Data compression via textual substitution
Journal of the ACM (JACM)
A Video Codec Based on R/D-Optimized Adaptive Vector Quantization
DCC '99 Proceedings of the Conference on Data Compression
2D-Pattern Matching Image and Video Compression
DCC '99 Proceedings of the Conference on Data Compression
RD-Optimization of Hierarchical Structured Adaptive Vector Quantization for Video Coding
DCC '00 Proceedings of the Conference on Data Compression
The macro model for data compression (Extended Abstract)
STOC '78 Proceedings of the tenth annual ACM symposium on Theory of computing
Overlap and channel errors in adaptive vector quantization for image coding
Information Sciences—Informatics and Computer Science: An International Journal
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Abstract: Constantinescu and Storer 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, modulo 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.