No-reference perceived image quality measurement for multiple distortions

Abstract

No-reference image quality assessment (NR-IQA) metrics play an important role in real-world multimedia applications. Accordingly, many NR-IQA metrics have been proposed in the literature. Most of these proposed metrics predict the visual quality only of images distorted with a single distortion, whereas in the real-world, digital images undergo various steps that contaminate them with multiple distortions before reaching consumers. However, only a few NR-IQA metrics have been proposed for multiple distortions. In this paper, we propose an NR-IQA metric for images distorted with blur and noise, which are added during the image acquisition and transmission process. In the proposed metric, perceived blur and noise scores are estimated separately. These scores are then mapped to the final quality score considering the perceived degree of each type of distortion, the nonlinear functions of the distortions, and the joint effects of the distortions. Weights for each mapping component are learned with subjective data input. Performance comparison of the proposed metric on the blur-noise image dataset of the LIVE multiply distorted (LIVEMD) image quality database confirm that the proposed metric is more effective than the state-of-the-art full-reference and NR IQA metrics.