Classified vector quantization for image compression using direction classification

Chou Chen Wang, Chin Hsing Chen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this paper, a classified vector quantization (CVQ) method using a novel direction based classifier is proposed. The new classifier uses a distortion measure related to the angle between vectors to determine the similarity of vectors. The distortion measure is simple and adequate to classify various edge types other than single and straight line types, which limit the size of image block to a rather small size. Simulation results show that the proposed technique can achieve better perceptual quality and edge integrity at a larger block size, as compared to other CVQs. It is shown when the vector dimension is changed from 16(4 × 4) to 64(8 × 8), the average bit rate can be reduced from 0.684 bpp to 0.191, whereas the PSNR degradation is only about 1.2 dB.

Original languageEnglish
Pages (from-to)535-542
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE82-A
Issue number3
Publication statusPublished - 1999 Jan 1

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this