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A Detail-Based Method for Linear Full Reference Image Quality Prediction

A Detail-Based Method for Linear Full Reference Image Quality Prediction

Abstract:

In this paper, a novel full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical differential mean opinion score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise.

Existing System:

IQA methods is that the relationship between metrics and the empirical DMOS scale is strongly non-linear. As a matter of fact, the Spearman Rank Order Correlation Coefficient (SROCC), often employed to compare the performance of different metrics, is insensitive to linearity issues. On the other hand, the linearity of the DMOS estimates is essential in applications, since the quality must be quantified at the end on the DMOS scale.

Proposed System:

The objective of the method presented in this paper is to solve the problems of unequal sensitivity to different impairments and of the a posteriori parametric linearization. Generally speaking, the approach followed here consists of the combination of different metrics, tailored to different impairments. Specifically, it stems from the consideration that most existing IQA methods treat image detail loss and spurious details in the same way. In other words, they do not distinguish between impairments caused by the loss of visual structures (depriving errors) or by the appearance of artifacts (meddling errors). However, this is at odds with common evidence, since detail losses and spurious details have a very different visual appearance.

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