Name
Abstract | ||
|---|---|---|
Image quality assessment (IQA) is of great importance to numerous image processing applications, and various methods have been proposed for it. In this paper, a Multi-Level Similarity (MLSIM) index for full reference IQA is proposed. The proposed metric is based on the fact that human visual system (HVS) distinguishes the quality of an image mainly according to the details given by low-level gradient information. In the proposed metric, the Prewitt operator is first utilized to get gradient information of both reference and distorted images, then the gradient information of reference image is segmented into three levels (3LSIM) or two levels (2LSIM), and the gradient information of distorted image is segmented by the corresponding regions of reference image, therefore we get multi-level information of these two images. Riesz transform is utilized to get corresponding features of different levels and the corresponding 1st-order and 2nd-order coefficients are combined together by regional mutual information (RMI) and weighted to obtain a single quality score. Experimental results demonstrate that the proposed metric is highly consistent with human subjective evaluations and achieves good performance. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.image.2013.08.006 | Sig. Proc.: Image Comm. |
Keywords | Field | DocType |
numerous image processing application,multi-level information,proposed metric,distorted image,full reference,low-level gradient information,reference image,image quality assessment,regional mutual information,gradient information,multi-level similarity index,prewitt operator,riesz transform | Computer vision,Quality Score,Computer science,Human visual system model,Reference image,Image processing,Image quality,Mutual information,Artificial intelligence,Prewitt operator,Riesz transform | Journal |
Volume | Issue | ISSN |
28 | 10 | 0923-5965 |
Citations | PageRank | References |
4 | 0.40 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hu Zhang | 1 | 5 | 1.09 |
| Yan Huang | 2 | 4 | 4.12 |
| Xi Chen | 3 | 4 | 0.74 |
| Dexiang Deng | 4 | 69 | 4.43 |