Name
Abstract | ||
|---|---|---|
The goal of Image Quality Assessment (IQA) is to design computational models that can automatically predict the perceived image quality consistent with human subjective ratings. In this paper, we propose a full reference IQA metric gradient weighted structural similarity (GW-SSIM) by incorporating the gradient information to the well-known IQA metric SSIM. Experimental results demonstrate that GW-SSIM can greatly improve the quality prediction accuracy and achieve the best performance among the SSIM-based methods by addressing SSIM's shortcomings. Additionally, incorporating the proposed gradient weighting (GW) map into peak-signal-to-noise ratio (PSNR) also makes it quite competitive to state-of-the-art IQA models, and this is meaningful since PSNR is still a widely adopted metric. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISCAS.2015.7169109 | 2015 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) |
Keywords | Field | DocType |
image quality assessment, gradient weighting map, structural similarity (SSIM), GW-SSIM, GW-PSNR | Data mining,Weighting,Computer science,Visualization,Image quality,Structural similarity,Computational model,Distortion | Conference |
ISSN | Citations | PageRank |
0271-4302 | 1 | 0.35 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiaohong Li | 1 | 212 | 9.33 |
| Yuming Fang | 2 | 1247 | 75.50 |
| Weisi Lin | 3 | 5366 | 280.14 |
| Daniel Thalmann | 4 | 4940 | 637.85 |