Image Quality Assessment by Quantifying Discrepancies of Multifractal Spectrums

Hang ZHANG  Yong DING  Peng Wei WU  Xue Tong BAI  Kai HUANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.9   pp.2453-2460
Publication Date: 2014/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2014EDP7036
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
image quality assessment,  multifractal spectrums,  statistical complexity feature,  human visual system,  

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Visual quality evaluation is crucially important for various video and image processing systems. Traditionally, subjective image quality assessment (IQA) given by the judgments of people can be perfectly consistent with human visual system (HVS). However, subjective IQA metrics are cumbersome and easily affected by experimental environment. These problems further limits its applications of evaluating massive pictures. Therefore, objective IQA metrics are desired which can be incorporated into machines and automatically evaluate image quality. Effective objective IQA methods should predict accurate quality in accord with the subjective evaluation. Motivated by observations that HVS is highly adapted to extract irregularity information of textures in a scene, we introduce multifractal formalism into an image quality assessment scheme in this paper. Based on multifractal analysis, statistical complexity features of nature images are extracted robustly. Then a novel framework for image quality assessment is further proposed by quantifying the discrepancies between multifractal spectrums of images. A total of 982 images are used to validate the proposed algorithm, including five type of distortions: JPEG2000 compression, JPEG compression, white noise, Gaussian blur, and Fast Fading. Experimental results demonstrate that the proposed metric is highly effective for evaluating perceived image quality and it outperforms many state-of-the-art methods.