Channel noise recovery of images through anti-Gray coding technique

In anti-Gray codes, binary indexes are assigned to code vectors in a way that maximizes the average quantization distortion introduced in the reproduced source vectors when a transmitted index is corrupted by channel noise. In this paper, anti-Gray coding is used for VQ encoded images. Since the average distortion at the receiver is maximized by the anti-Gray coding technique, errors can be easily detected. To detect channel errors, we first classify an image into uniform and edge regions. Then we propose a mask to detect channel errors based on the image classification (uniform and edge regions) and anti-Gray code characteristics. We also mathematically derive criteria for error detection based on image classification. Once error indexes are detected, the recovered indexes 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 the detection results with less than 0.1% probability of error and more than 86.3% probability of detection at random bit error rate 0.1%. For those undetected errors, they appear invisible. In addition, the proposed approach provides a better PSNR performance than other similar techniques and pseudo-Gray codes for images under noisy channels