No‐reference image quality assessment via structural information fluctuation. Issue 2 (6th January 2020)

• Record Type: Journal Article
• Authors: Yang, Xichen; Wang, Tianshu; Ji, Genlin
• Is Part Of: IET image processing. Volume 14:Issue 2(2020)
• Page Start: 384
• Page End: 396
• Publication Date: 2020-01-06
• Subjects: feature extraction -- visual perception -- image processing -- regression analysis -- support vector machines; structural features -- IQA model -- natural image -- SCI -- reference image quality assessment -- structural information fluctuation -- meaningful research topic -- high‐quality image -- image structural information -- human visual system -- IQA method -- human visual perception -- screen content image -- structural information analysis -- GFM -- grey‐scale fluctuation direction map; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0750
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Image quality assessment (IQA) is a meaningful research topic to meet the increasing demand of high‐quality image. The degradation of image quality will cause changes in image structural information. Meanwhile, human visual system is sensitive to changes in structural information. This finding motivates us to utilise structural information for proposing IQA method which is consistent with human visual perception. Recently, IQA methods are mainly focused on individual image type, e.g. natural image or screen content image (SCI), thus, the authors proposed a novel no‐reference IQA method which can be suitable for both natural image and SCI. The proposed method is based on structural information analysis. For each image, they first obtain the grey‐scale fluctuation maps (GFMs) in four detection directions. After that, the grey‐scale fluctuation direction map (GFD) of certain image can be acquired via its GFMs. Based on the GFMs and GFD, the structural features of each image are extracted, and then collected and transformed to feature vectors. Subsequently, the IQA model is trained by support vector regression. The experimental results on the public databases demonstrate the proposed method can predict image quality accurately for both natural image and SCI, and the performance is competitive with prevalent methods.