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Noise reduction of VQ encoded images through anti-gray coding

机译:通过防灰编码降低VQ编码图像的噪声

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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.
机译:VQ编码图像的降噪是通过提出的防灰编码(AGC)和噪声检测与校正方案实现的。在AGC中,二进制索引以这样的方式分配给代码矢量:代码矢量的1-b邻居尽可能远。为了检测通道错误,我们首先将图像分类为均匀区域和边缘区域。然后,我们提出了一种基于图像分类(均匀或边缘区域)和AGC特性的掩模来检测信道错误。我们还基于图像分类从数学上推导了错误检测标准。一旦检测到错误索引,就可以通过最小化VQ编码图像中跨块边界的灰度转换,轻松地从“候选集”中选择恢复的索引。仿真结果表明,该技术以0.1%的随机误码率提供了小于0.1%的错误概率和大于86.3%的检测概率,而未检测到的错误是不可见的。另外,提出的检测和校正技术将图像质量(与AGC编码的图像质量相比)提高了3.9 dB。

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