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Noise reduction of VQ encoded images through anti-gray coding

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3 Author(s)
Kuo, C.J. ; Signal & Med. Labs., Nat. Chung Cheng Univ., Chiayi, Taiwan ; Lin, C.H. ; Yeh, C.H.

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 the 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 a random bit error rate of 0.1%, while the undetected errors are invisible. In addition, the proposed detection and correction techniques improve the image quality (compared with that encoded by AGC) by 3.9 dB

Published in:

Image Processing, IEEE Transactions on  (Volume:8 ,  Issue: 1 )