Noise reduction of VQ encoded images through anti-gray coding

Chung J. Kuo*, Chien H. Lin, Chia H. Yeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Noise reduction of VQ encoded images is achieved through the proposed anti-gray coding (AGC) and noise detection and correction scheme. In AGC, binary indices are assigned to the codevector in such a way that the 1-b neighbors of a code vector are as far apart as possible. To detect the channel errors, we first classify an image into uniform and edge regions. Then we propose a mask to detect the channel errors based on the image classification (uniform or edge region) and characteristics of AGC. We also mathematically derive a criterion for error detection based on the image classification. Once error indices are detected, the recovered indices can be easily chosen from a "candidate set" by minimizing the gray-level transition across the block boundaries in a VQ encoded image. Simulation results show that the proposed technique provides detection results with smaller than 0.1% probability of error and more than 86.3% probability of detection at random bit error rate 0.1%, while the undetected errors are invisible. In addition, the proposed detection and correction techniques improve image quality (compared with that encoded by AGC) by 3.9 dB.

Original languageEnglish
Pages (from-to)33-40
Number of pages8
JournalIEEE Transactions on Image Processing
Issue number1
Publication statusPublished - 1999
Externally publishedYes


  • Gray code
  • Noise detection and correction

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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