Research On Content Based Image Adaptive Compression

Image compression is the key problem in digital image transmission and storage , Adaptive compression is a useful method to imcrease the compression ratio. The discussion of this thesis is mainly the adaptive compression based on the content characters of images whose purpose is to improve the compression performance and the decoded images performance. The algorithm is mainly composed of adaptive partition , adaptive quantization and deblocking algorithm.The adaptive partition based on the SUSAN operator is divided into smooth block and non-smooth block according to the edge information. The edge information is used to measure the content characters. The biggest size of block is 64*64,and the smallest one is 8*8.The adaptive block adopts to quadtree segment .The method of edge detection is used to partition adaptively. Experiments have proved that the bigger the size of the smooth block is, the better the performance of the compression is.Adaptive quantization is based on the important coefficients which is defined as the content characters of images for non-smooth block. Adaptive quantization for smooth block modifies the quantization step to reserve the important coefficients . Adaptive quantization for non-smooth block resort the standard quantization step to reserve the important coefficients. But these important coefficients are needed to be presented by address information. In this thesis we make the address information aggregate to be helpful to compress. By aggregating the address, the address information is cut down and the compression performance advances 20%-25% compared to standard JPEG algorithm.As the result of adaptive partition, it exhibit noticeable blocking artifact near the block boundaries. The deblocking algorithm based on HVS is used to recover the missing image blocks. Experiments have proved the algorithm of this thesis is super to the standard JPEG algorithm in the improvement of PSNR and compress performance.