Contrast and Visual Saliency Similarity-Induced Index for Assessing Image Quality

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.

Full Reference Image Quality Assessment Based on Visual Salience With Color Appearance and Gradient Similarity

“…To compute the PLCC and RMSE indices, a logistic regression is adopted to get the same scale values with subjective judgments by using: (13) where β 1 , . .…”

“…In SDSP [23], frequency prior, color prior and location prior were integrated as a saliency algorithm. Recently, a perceptual IQA model was developed utilizing Contrast and Visual Saliency Similarity (CVSS) [13] maps. In CVSS, the spectral residual method [24] was operated as saliency map generator.…”

“…In the past few decades, multiple objective IQA models have been developed to evaluate image quality [3]. There are three main well-established frameworks according to the availability of a reference image: full reference (FR) [4]- [13], reduced reference (RR), and no reference (NR) [14] or blind IQA.…”

Center-Emphasized Visual Saliency and a Contrast-Based Full Reference Image Quality Index

“…There are a lot of saliency detection techniques available [32], among which spectral residual saliency detection [22] is a very fast approach. We adopt the saliency map generator described in the SR-SIM [24] and the visual saliency plus contrast (VSP) [26] approaches.…”

“…The spectral residual similarity index (SR-SIM) [24] uses the spectral residual saliency, which makes the approach very fast while maintaining a competitive correlation with the mean opinion score. Combining VS with other features has also become popular [25,26] [5] IW-SSIM Information content extraction Weighted by information content Main emphasis is on information content extraction which is applicable to other methods as well, uses image pyramid.…”