Image Quality Assessment Using Sparse Representation in ICA Domain
A novel metric for full-reference image quality assessment (IQA) is proposed in this paper. Based on the sparse representation in independent component analysis (ICA) domain, the image basis is generated from natural images adaptively, which coincides with the characteristics of human vision system (HVS). In order to extract the feature vector, a hybrid norm optimization strategy is introduced for achieving more stable computational performances. The proposed IQA metric is calculated as a correlation coefficient between the two feature vectors from reference and distorted images, respectively. Experimental results on the LIVE Database Release 2 demonstrate that the proposed metric can achieve competitive performances as compared to the well-known structural similarity (SSIM) metric.
Cheng Cheng Hanli Wang
Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, S Key Laboratory of Embedded System and Service Computing, Ministry of Education,Tongji University, Sh
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-4
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）