Lossless Image Compression via Classified Rearrangement and Group Encoding
This paper proposes a lossless image compression algorithm based on classified rearrangement and group encoding. The algorithm exploits the effects of grayscale distribution of the original image on the compression ratio, and first classifies all the pixels into several classes in terms of the probability statistics of their grayscales, increasing the correlation among pixels with cohesion of the pixels of similar grayscales, and then rearranges all classes of pixels along inner-block and inter-block according to Hilbert curves, further increasing the correlation among pixels by reordering their spatial distribution, and last respectively does group encoding to every class of pixels on basis of their grayscale probabilities, which makes the most of the correlation among pixels to achieve lossless compression of the original image. Group encoding assigns one-to-one codeword of variable length to each grayscale in accordance with its probability value, and the codeword is comprised of group number and inner-group representation two parts. The simulation results of multiple standard test images demonstrate that the algorithm is of good performance with high encoding efficiency and compression ratio, low complexity and easy-implementation.
grayscale classification Hilbert curve pixel rearrangement group encoding lossless compression
Huijie Guo Baojun Zhao
School of Information and Electronics, Beijing Institute of Technology Beijing, China
国际会议
成都
英文
1521-1525
2010-12-17（万方平台首次上网日期，不代表论文的发表时间）