Still image compression with mean-residual domain adaptive resolution vector quantization technique

Takahiro Nakayama, Masahiro Konda, Koji Takeuchi, Koji Kotani, Tadahiro Ohmi

Research output: Contribution to journalArticlepeer-review

Abstract

With the glowing importance of an advanced image processing methods for digital image, an efficient compression algorithm is increasingly important. In this paper we present progressive image compression algorithm based on Vector Quantization (VQ). Mean-Residual domain Adaptive Resolution Vector Quantization technique (MAR-VQ) that combines the adaptive resolution method with the mean-residual method has been developed. The adaptive resolution method changes the resolution of image adaptively according to the form of a pixel block texture in order to increase the performance of compound image compression. In addition, the mean-residual method was introduced as one of the additional techniques to increase continuous-tone image quality by VQ. As a result, MAR-VQ can realize much superior compression performance than the worldwide standard JPEG 2000.

Original languageEnglish
Pages (from-to)231-243
Number of pages13
JournalIntelligent Automation and Soft Computing
Volume12
Issue number3
DOIs
Publication statusPublished - 2006 Jan

Keywords

  • Adaptive resolution conversion
  • Image compression
  • Mean-residual method
  • Vector quantization

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Still image compression with mean-residual domain adaptive resolution vector quantization technique'. Together they form a unique fingerprint.

Cite this