Name
Abstract | ||
|---|---|---|
Since human vision has much greater resolutions at the center of our visual field than elsewhere, different criteria of quality assessment should be applied on the image areas with different visual resolutions. This paper proposed a foveation-based image quality assessment method which adopted different sizes of windows in quality assessment for a single image. Visual salience models which estimate visual attention regions are used to determine the foveation center and foveation resolution models are used to guide the selection of window sizes for the areas over spatial extent of the image. Finally, the quality scores obtained from different window sizes are pooled together to get a single value for the image. The proposed method has been applied to IQA metrics, SSIM, PSNR, and UQI. The result shows that both Spearman and Kendall correlation coefficients can be improved significantly by our foveation-based method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/VCIP.2014.7051495 | VCIP |
Keywords | Field | DocType |
video signal processing,human visual system,foveation-based image quality assessment,visual salience model,image resolution,foveation center models,image quality assessment,foveation resolution models,visual salience models,visual attention regions,foveation,visual perception,signal to noise ratio,mean opinion score,databases,structural similarity,image quality,mean square error,peak signal to noise ratio | Computer vision,Similitude,Human visual system model,Computer science,Signal-to-noise ratio,Reference image,Mean squared error,Image quality,Mean opinion score,Artificial intelligence,Luminance | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen-Jiin Tsai | 1 | 174 | 19.57 |
| Yi-Shih Liu | 2 | 0 | 0.34 |