Rate-distortion optimized image compression using generalized principal component analysis

Dohyun Ahn, Chang-Su Kim, Sang Uk Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A novel image compression algorithm based on generalized principal component analysis (GPCA) is proposed in this work. Each image block is first classified into a subspace and is represented with a linear combination of the basis vectors for the subspace. Therefore, the encoded information consists of subspace indices, basis vectors and transform coefficients. We adopt a vector quantization scheme and a predictive partial matching scheme to encode subspace indices and basis vectors, respectively. We also propose a rate-distortion optimized quantizer to encode transform coefficients efficiently. Simulation results demonstrate that the proposed algorithm provides better compression performance than JPEG, especially at low bitrates.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesII5-II8
Publication statusPublished - 2006
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: 2006 May 142006 May 19

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Other

Other2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
CountryFrance
CityToulouse
Period06/5/1406/5/19

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Rate-distortion optimized image compression using generalized principal component analysis'. Together they form a unique fingerprint.

Cite this