A New Image Structural Similarity Metric Based on K-L Transform

摘要：

　　Recently,structural similarity image metric (SSIM) becomes the most popular model for image quality assessment (IQA).The idea behind SSIM is that natural images are highly structured,and estimate a general similarity of the image pairs from luminance,contrast and structure comparison.A novel similarity measure based on K-L transform is presented in this paper.It combines edge and texture components to provide a hierarchical description of image structure.We validate the performance of our algorithm with an extensive subjective study involving two sets of compressed images,the JPEG and the JPEG2000 images at the LIVE website.The experimental results show that the obtained quality metric had a high correlation with the subjective measure and outperforms SSIM.

关键词： K-L transform SSIM human visual system edge feature texture feature

作者: Cheng Jiang Fen Xiao Xiaobo He

作者单位: The College of Information Engineering,Xiangtan University,Xiangtan,Hunan,41105,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 159-168

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）