Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments

In the research field of image processing, mean squared error (MSE) and peak signal-to-noise ratio (PSNR) are extensively adopted as the objective visual quality metrics, mainly because of their simplicity for calculation and optimization. However, it has been well recognized that these pixel-based difference measures correlate poorly with the human perception. Inspired by existing works

, in this paper we propose a novel algorithm which separately evaluates detail losses and additive impairments for image quality assessment. The detail loss refers to the loss of useful visual information which affects the content visibility, and the additive impairment represents the redundant visual information whose appearance in the test image will distract viewer's attention from the useful contents causing unpleasant viewing experience. To separate detail losses and additive impairments, a wavelet-domain decoupling algorithm is developed which can be used for a host of distortion types. Two HVS characteristics, i.e., the contrast sensitivity function and the contrast masking effect, are taken into account to approximate the HVS sensitivities. We propose two simple quality measures to correlate detail losses and additive impairments with visual quality, respectively. Based on the findings in

that observers judge low-quality images in terms of the ability to interpret the content, the outputs of the two quality measures are adaptively combined to yield the overall quality index. By conducting experiments based on five subjectively-rated image databases, we demonstrate that the proposed metric has a better or similar performance in matching subjective ratings when compared with the state-of-the-art image quality metrics.