Contrast and visual saliency similarity-induced index for assessing image quality
This work addresses the need for effective and efficient image quality assessment tools for applications in image processing and computer vision, though it is incremental as it builds on existing IQA methods.
The study tackled the problem of perceptual image quality assessment (IQA) by developing a metric that uses contrast and visual saliency to predict human judgments, achieving the best correlation on benchmark databases (LIVE, TID2008, CSIQ) and improved efficiency compared to existing models.
Image quality that is consistent with human opinion is assessed by a perceptual image quality assessment (IQA) that defines/utilizes a computational model. A good model should take effectiveness and efficiency into consideration, but most of the previously proposed IQA models do not simultaneously consider these factors. Therefore, this study attempts to develop an effective and efficient IQA metric. Contrast is an inherent visual attribute that indicates image quality, and visual saliency (VS) is a quality that attracts the attention of human beings. The proposed model utilized these two features to characterize the image local quality. After obtaining the local contrast quality map and the global VS quality map, we added the weighted standard deviation of the previous two quality maps together to yield the final quality score. The experimental results for three benchmark databases (LIVE, TID2008, and CSIQ) demonstrated that our model performs the best in terms of a correlation with the human judgment of visual quality. Furthermore, compared with competing IQA models, this proposed model is more efficient.