基於視覺解析度的影像品質評估方式

Abstract

由於人眼視覺對於視網膜中心的解析度較其他區域相對高，基於這個理論，我們在做影像品質評估時，在一張影像中不同區域使用不一樣的標準，對於視覺解析度高的區域採較高的品質評估標準，反之，則採較低的標準。 本篇論文利用不同的視窗大小來檢驗同一張影像中不同的區域，以實現一個基於視覺解析度的影像品質評估方式。而視覺專注的影像技術可以幫助我們區分哪些區域使用多大的視窗尺寸。最後影像品質的分數由不同的視窗大小算出來的分數，並分別套用不同的權重結合而成。我們所提出來基於視覺解析度的影像品質評估方法，已應用在PSNR、SSIM及UQI這三種影像品質評估方法上，並且在Spearman及Kendall的相關係數上有顯著的提升。

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. My thesis proposed a foveation-based image quality assessment method which adopted different sizes of windows in quality assessment for a single image.Visual salience modeling techniques which estimate visual attention regions are adopted 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, PSNR and SSIM, and the result shows that both Spearman and Kendall correlation coefficients can be improved significantly by our foveation-based method.