A Fast Image Encoding Algorithm Based on Correlation Predictive Vector Quantization
一种快速相关预测矢量量化的图像编码算法

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.