Vector quantization using matrix decompositions of codebooks for image coding

Jar Ferr Yang, Chiou Liang Lu, Po Wen Chin

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

Methods of using vector quantization combined with singular value decomposition (SVD) and simplified least square estimation (LSE) compensation are proposed. The complexity of computation is decreased by determining a simpler scheme to obtain the same optimal estimated singular values as the LSE. With the assistance of the matrix-decomposed codebooks, edge degradation and compression rate can be improved by using a few least-square-compensated singular values. Avoiding the high computational complexity of SVD during the encoding and decoding procedures and achieving good quality of image coding are the main contributions of the present work.

Original languageEnglish
Pages (from-to)308-311
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume1
Publication statusPublished - 1991 Dec 1
Event1991 IEEE International Symposium on Circuits and Systems Part 1 (of 5) - Singapore, Singapore
Duration: 1991 Jun 111991 Jun 14

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Vector quantization using matrix decompositions of codebooks for image coding'. Together they form a unique fingerprint.

Cite this