Abstract
Objective image quality metrics (IQMs) potentially benefit from the addition of visual saliency. However, challenges to optimizing the performance of saliency-based IQMs remain. A previous eye-tracking study has shown that gaze is concentrated in fewer places in images with highly salient features than in images lacking salient features. From this, it can be inferred that the former are more likely to benefit from adding a saliency term to an IQM. To understand whether these ideas still hold when using computational saliency instead of eye-tracking data, we first conducted a statistical evaluation using 15 state-of-the-art saliency models and 10 well-known IQMs. We then used the results to devise an algorithm, which adaptively incorporates saliency in IQMs for natural scenes, based on saliency dispersion. Experimental results demonstrate that this can give significant improvements.
Highlights
Image quality metrics (IQMs) lie at the heart of algorithms to automatically predict perceived image quality [1]
To make better use of saliency in IQMs, a sophisticated integration strategy is needed, taking into account image content, in terms of the dispersion of saliency. The contributions of this papers are: 1) a statistical evaluation of whether conclusions concerning the content-dependent nature of benefits of adding saliency information to an IQM, determined from eye-tracking data, still hold when computational saliency is used in its place and 2) an algorithm that can provide a reliable proxy for inter-observer agreement (IOA), for use in content adaptive IQM which incorporates computational saliency
We compared its performance between no use of saliency, fixed use of saliency, and adaptive use of saliency according to saliency dispersion
Summary
Image quality metrics (IQMs) lie at the heart of algorithms to automatically predict perceived image quality [1]. Our previous research based on eye tracking [14] revealed that the inter-observer agreement (IOA) for human fixations—the degree of agreement between observers freely viewing the same visual stimulus—is strongly image content dependent This measure predicts the extent to which a certain image may profit from adding saliency information to an IQM. To make better use of saliency in IQMs, a sophisticated integration strategy is needed, taking into account image content, in terms of the dispersion of saliency The contributions of this papers are: 1) a statistical evaluation of whether conclusions concerning the content-dependent nature of benefits of adding saliency information to an IQM, determined from eye-tracking data, still hold when computational saliency is used in its place and 2) an algorithm that can provide a reliable proxy for IOA, for use in content adaptive IQM which incorporates computational saliency
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