Complex number‐based image quality assessment using singular value decomposition. Issue 2 (1st February 2016)

• Record Type: Journal Article
• Authors: Yong, Wang; Yuqing, Wang; Xiaohui, Zhao
• Is Part Of: IET image processing. Volume 10:Issue 2(2016)
• Page Start: 113
• Page End: 120
• Publication Date: 2016-02-01
• Subjects: image processing -- singular value decomposition; complex number‐based image quality assessment -- singular value decomposition -- IGA metric -- energy visual map -- structure visual map -- structure similarity vectorial expansion; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0937
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In this study, the combining strategies are considered to be effective tools to improve the performance of the image quality assessment (IQA) metrics. A new metric is proposed to evaluate the quality of test images of combined degradation and individual degradation. The complex numbers are used to describe the image structure in the proposed method. On the basis of that, the properties of the classical IQA method based on singular value decomposition are analysed. The difference between energy visual map and structure visual map is shown. The complex‐number‐based approach is different from the classical scalar‐based techniques, which are insufficient to describe image structure. The proposed C_SVDQ metric can be considered as a vectorial expansion of structure similarity. In the experiments, an extensive comparison between the proposed C_SVDQ and other IQA metrics on image quality database was performed. Both the overall tests and the individual distortion tests show the superiority of this new approach in IQA.