A fast full search equivalent encoding method for vector quantization by using appropriate features

Z. Pan, K. Kotani, T. Ohmi

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

6 Citations (Scopus)

Abstract

The encoding process of vector quantization (VQ) is very heavy and it constrains VQ's application a great deal. In order to speed up VQ encoding, it is most important to avoid unnecessary Euclidean distance computation (k-D) as much as possible by the difference check that uses simpler features (low dimensional) while winner searching is going on. Sum (1-D) and partial sums (2-D) are used together as the appropriate features in this paper because they are the first 2 simplest features. Then, sum difference and partial sum difference are computed as the estimations of Euclidean distance and they are connected to each other by the Cauchy-Schwarz inequality so as to reject a lot of codewords. For typical standard images with very different details (Lena, F-16, Pepper and Baboon), the final must-do Euclidean distance computation using the proposed method can be reduced to less than 10% as compared to full search (FS) meanwhile keeping the PSNR not degraded.

Original languageEnglish
Title of host publicationProceedings - 2003 International Conference on Multimedia and Expo, ICME
PublisherIEEE Computer Society
PagesII261-II264
ISBN (Electronic)0780379659
DOIs
Publication statusPublished - 2003 Jan 1
Event2003 International Conference on Multimedia and Expo, ICME 2003 - Baltimore, United States
Duration: 2003 Jul 62003 Jul 9

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Other

Other2003 International Conference on Multimedia and Expo, ICME 2003
CountryUnited States
CityBaltimore
Period03/7/603/7/9

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

Fingerprint Dive into the research topics of 'A fast full search equivalent encoding method for vector quantization by using appropriate features'. Together they form a unique fingerprint.

Cite this