Still image compression with adaptive resolution vector quantization technique

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

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


A novel image compression algorithm based on vector quantization (VQ) technique is proposed in this paper. Adaptive resolution VQ (AR-VQ) method, which is composed of three key techniques, i.e., the edge detection, the resolution conversion, and the block alteration, can realize much superior compression performance than the JPEG and the JPEG-2000. On the compression of the XGA (1024x768 pixels) images including text, for instance, there exist an overwhelming performance difference of 5 to 40 dB in compressed image quality. In addition, we propose a systematic codebook design method of 4x4 and 2x2 pixel blocks for AR-VQ without using learning sequences. According to the method, the codebook can be applied to all kinds of images and exhibits equivalent compression performance to the specific codebooks created individually by conventional learning method using corresponding images.

Original languageEnglish
Pages (from-to)155-165
Number of pages11
JournalIntelligent Automation and Soft Computing
Issue number2
Publication statusPublished - 2004 Jan


  • Adaptive resolution conversion
  • Codebook design
  • Still image compression
  • Vector quantization

ASJC Scopus subject areas

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


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

Cite this