Name
Abstract | ||
|---|---|---|
Feature extraction is probably the most important stage in image quality evaluation-effective features can well reflect the quality of digital images and vice versa. As a non-redundant sparse representation, contourlet transform can effectively reflect visual characteristics of images, and it can be employed to perceptually capture the difference between images. Motivated by this, this paper first proposes an objective reduced-reference image quality evaluation metric based on contourlet transform. Experiments demonstrate that this new objective metric achieves consistent image quality evaluation results with what gained by subjective evaluation. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.neucom.2007.12.031 | Neurocomputing |
Keywords | Field | DocType |
objective reduced-reference image quality,non-redundant sparse representation,feature extraction,consistent image quality evaluation,digital image,subjective evaluation,image quality evaluation-effective feature,wavelet-based contourlet,new objective metric,important stage,visual characteristic,image quality,sparse representation,digital image processing,visual perception,contourlet transform | Top-hat transform,Computer vision,Pattern recognition,Computer science,Image analysis,Image quality,Digital image,Feature extraction,Artificial intelligence,Digital image processing,Contourlet,Wavelet | Journal |
Volume | Issue | ISSN |
72 | 1-3 | Neurocomputing |
Citations | PageRank | References |
15 | 0.82 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinbo Gao | 1 | 5534 | 344.56 |
| Wen Lu | 2 | 25 | 3.35 |
| Xuelong Li | 3 | 15049 | 617.31 |
| Dacheng Tao | 4 | 19032 | 747.78 |