Rate-Controllable Near-Lossless Image Compression And Its FPGA Implementation

Digital image compression is a basic method to solve the difficulty of storing and transporting the large amount of the image data. The methods of image compression could be summed up as the lossy and lossless compression. The lossy compression method leads to the information lost in same sense, so through this way, we could get the higher compression ratios. Comparatively, the information lost is forbidden in the lossless compression, and we could not get high compression ratios. In many fields, such as medical imaging and satellite imaging, it is very necessary to trade off between compression ratios and distortion, so near-lossless compression could be considered. Near-lossless compression, in which compression ratio is better than lossless mode remaining quite good quality of decoded images, is a compromise between the lossy compression method and lossless compression method.The JPEG-LS compression algorithm is the international standard for lossless and near-lossless compression of continuous-tone still images. It has a great compression capability with low complexity, but the compression ratios are not controlled for different images. In this paper, we study the process of the near-lossless compression mode of JPEG-LS, and proposed a block-based compression scheme. Then, we propose the rate control scheme for the block-based near-lossless compression algorithm, by utilizing the multiple information loss level for different areas of the image according to the analysis of the local texture feature. For a special engineering application, the hardware implementation of the proposed algorithm based on FPGA is designed with Verilog HDL in the paper.Experimental results reveal that the block-based rate-controllable near-lossless compression algorithm is very effective, which is able to control the compression ratio very close to the target ratio with high image quality. The results also reveal that the hardware of the algorithm fills the need of real-time image compression with low cost.