Soft Computing

, Volume 21, Issue 5, pp 1145–1155 | Cite as

Image quality assessment based on multiscale fuzzy gradient similarity deviation

Methodologies and Application

Abstract

Although a great number of objective methods for assessing perceptual image quality have been proposed, their performances on different distortion types are still unsatisfactory. It is a great challenge for image quality assessment (IQA) that distorted images may have similar visual perception even if their distortion types and magnitudes are totally different, because some structural changes caused by distortions are visually imperceptible. In this paper, we propose a novel full-reference IQA (FR-IQA) scheme based on multiscale fuzzy gradient similarity deviation (MFGSD), where fuzzy inference system is introduced to reduce the negative impact of imperceptible distortions, and the standard deviation of fuzzy gradient similarity is utilized to measure their quality distinction. Extensive experiments are conducted on two publicly available image databases, and compared with many existing state-of-art FR-IQA schemes, and the proposed MFGSD has a better performance on different distortion types and strengths.

Keywords

Fuzzy inference system Image quality assessment (IQA) Gradient similarity Multiscale processing Human visual system (HVS) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.College of Computer ScienceChongqing UniversityChongqingChina

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