一种快速相关预测矢量量化的图像编码算法

矢量量化是近年来图像压缩研究中的重要技术，在该技术中，减小编码运算时间和降低平均编码比特率是当前研究的重要问题，目前，已经提出了许多快速编码算法。为了进一步减少图像矢量量化编码的时间和降低编码比特率，提出了一种超前相关预测与快速搜索相结合的快速矢量量化编码算法。该算法在对当前图像块完成编码后，再根据该图像块与相邻的未编码图像块的相关性来预测相邻块的编码值，如果预测成功，则用低比特率表示编码值；否则用绝对误差不等式删除(AEI)算法来求得高比特率编码值，以减少总编码时间和降低平均比特率。测试结果表明，该算法比传统的穷尽搜索算法的编码速度快，且比特率低，同时对编码质量的影响很小。

A Fast Image Encoding Algorithm Based on Correlation Predictive Vector Quantization

Vector Quantization (VQ) is an important technology of image compression research in the recent years. Reducing encoding computation time and cutting down average encoding bit rates are the two important problems of its current research. In the present, many fast search encoding algorithm based on VQ are proposed. In order to get less encoding time and lower average encoding bit rates, a fast encoding algorithm, which integrates advance correlation predictive process with a fast search encoding algorithm, has been presented in this paper. After the current image block has been encoded, the encoding value of near neighbor image blocks is predicted, by virtue of their correlation to the current block. If predictive successes, the encoding value needs few bits to denote, otherwise it needs complex computation by Absolute Error Inequality Elimination algorithm (AEI), and more bits to transfer. By this proposed way, the total encoding time decreases, and the total encoding bits saves. The simulation experiments show that the proposed algorithm consumes less time to encode, and needs lower average bit rates to transfer encoding results, against full search algorithm (FS), while its encoding performance is close to that of full search algorithm.