图像活跃度在图像分解中的应用  

The application of IAM in image decomposition

作　　者：崔朝辉[1] 刘冀伟[1] 王志良[1] 张晓星[1] 

机构地区：[1]北京科技大学信息工程学院,北京100083

出　　处：《中国图象图形学报》2011年第5期710-719,共10页Journal of Image and Graphics

基　　金：国家高技术研究发展计划(2007AA01Z160);国家自然科学基金项目(60903067)

摘　　要：在综合分析当前图像压缩算法的基础上,提出新的基于分层变块大小分解的图像压缩构想。JPEG、JPEG2000、分形作为当前最为流行的3种静态图像压缩算法,在对不同的图像进行相同倍率的压缩时,表现出同样的性能趋势:视觉上越复杂的图像,恢复图像的质量越低。经过大量实验发现,3种算法的压缩性能均与同一个指标存在明确关系——图像活跃度量(IAM)。根据图像不同区域的复杂程度不同,采用IAM和相似度作为性能指标,利用粒子群优化(PSO)算法求解最优近似图像,实现对图像的分层变块大小分解(SVBD),将图像中相同复杂特性的区块归为一类。该分解方式符合人类认知图像内容的特点,为提高压缩性能创造了有利条件。A concept of image compression based stratified variable blocksize decomposition is proposed after analyzing current image compression algorithms compreebensively. As the current most popular methods for static image compression, JPEG, JPEG2000 and fractal perform similarly when compressing different images with the same compression ratio： the more visually complex an image is, the lower a restored image quality is. After a large number of experiments, previous work shows that there is a clear relationship between all the three methods and image activity measure （ IAM）. According to the complexity of different image regions, stratified variable b]ocksize decomposition （SVBD） is achieved, using IAM and similarity as performance index. Particle swarm optimization （PSO） algorithm is used to find optimal approximation of the image blocks. Finally, by classifying the image blocks, some improvement to compression quality can be made.

关 键 词：图像活跃度量 图像分解 分层 变块大小 粒子群优化 图像压缩 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]