Abstract
Visual saliency plays a significant role in image quality assessment. Image distortions cause shift of saliency from its original places. Being able to measure such distortion-included saliency variation (DSV) contributes towards the optimal use of saliency in automated image quality assessment. In our previous study a benchmark for the measurement of DSV through subjective testing was built. However, exiting saliency similarity measures are unhelpful for the quantification of DSV due to the fact that DSV highly depends on the dispersion degree of a saliency map. In this paper, we propose a novel similarity metric for the measurement of DSV, namely MDSV, based on convex optimization method. The proposed MDSV metric integrates the local saliency similarity measure and the global saliency similarity measure using the function of saliency dispersion as a modulator. We detail the parameter selection of the proposed metric and the interactions of sub-models for the convex optimization strategy. Statistical analyses show that our proposed MDSV outperforms the existing metrics in quantifying the image quality induced saliency variation.
Highlights
S ALIENCY – the scene-driven bottom-up selective visual attention mechanism of the human visual system – has been widely studied in the area of visual quality assessment [1]–[6]
Based on the proposed convex optimization method of equation (6), we define a metric for distortion-induced saliency variation, namely MDSV1, combing the local saliency similarity measure AUC-Judd and global saliency similarity measure CC
It is worth noting that MDSV1 produces better performance than any of the individual sub-models, meaning the proposed convex optimization method has proven efficacy that may be caused by the dispersion related convex modulator
Summary
S ALIENCY – the scene-driven bottom-up selective visual attention mechanism of the human visual system – has been widely studied in the area of visual quality assessment [1]–[6]. To measure such so-called distortion-induced saliency variation (DSV) – the similarity between the original saliency (e.g., (e) in Figure 1) and the deviated saliency (e.g., (f), (g) or (h) in Figure 1) – a benchmark was established in [17] In this benchmark, the difference mean saliency variation score (DMSS) per saliency map in the SIQ288 database is yielded via a subjective study. THE DSV BENCHMARK The benchmark of the distortion-induced saliency variation (DSV) [17] is based on the SIQ288 database [16], which contains images of varying quality (i.e., 18 pristine images and each distorted by 5 distortion types and 3 distortion levels) and their corresponding saliency maps rendered from ground truth human fixations.
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