Contrast and visual saliency similarity-induced index for assessing image quality

TL;DR

This paper introduces a new image quality assessment model that combines contrast and visual saliency features, achieving high correlation with human judgment while maintaining computational efficiency.

Contribution

The study proposes a novel IQA metric that effectively and efficiently combines contrast and visual saliency features for better image quality evaluation.

Findings

Outperforms existing models in correlation with human judgment on benchmark datasets.

Demonstrates superior efficiency compared to competing IQA models.

Achieves the best results on LIVE, TID2008, and CSIQ databases.

Abstract

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.