Contrast and visual saliency similarity induced index for image quality assessment

Perceptual image quality assessment (IQA) defines/utilizes a computational model to assess the image quality in consistent with human opinions. A good IQA model should consider both the effectiveness and efficiency, while most previous IQA models are hard to reach simultaneously. So we attempt to make another effort to develop an effective and efficiency image quality assessment metric. Considering that contrast is a distinctive visual attribute that indicates the quality of an image, and visual saliency (VS) attracts the most attention of the human visual system, the proposed model utilized these two features to characterize the image local quality. After obtaining the local contrast quality map and global visual saliency quality map, we add the weighted standard deviation of the previous two quality maps together to yield the final quality score. The experimental results on three benchmark database (LIVE, TID2008, CSIQ) showed that the proposed model yields the best performance in terms of the correlation with human judgments of visual quality. Furthermore, it is more efficient when compared with other competing IQA models.