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access icon openaccess Hybrid NSS features for no-reference image quality assessment

  • Author(s): Min Qin 1 ; Xiaoxin Lv 1 ; Xiaohui Chen 2 ; Weidong Wang 1
    • View affiliations
    • Affiliations: 1: Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, People's Republic of China ;
      2: Key Laboratory of Wireless-Optical Communications, University of Science and Technology of China, Hefei, People's Republic of China
  • Source: Volume 11, Issue 6, June 2017, p. 443 – 449
    DOI:  10.1049/iet-ipr.2016.0411 , Print ISSN 1751-9659, Online ISSN 1751-9667
This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Received 01/06/2016, Accepted 19/03/2017, Revised 09/03/2017, Published 28/03/2017

A novel general-purpose no-reference image quality assessment (NR-IQA) model utilising hybrid natural scene statistics (HNSS) is proposed. Distinguished from existing NR-IQA approaches, the new model combines the statistics of locally mean subtracted and contrast normalised coefficients in the spatial domain and the statistics of image patch coefficients in a codebook space, which is constructed by codebooks extracted from pristine images using K-Means. The authors demonstrate that the coefficients in the codebook space keep the NSS characteristics as same as these in the spatial domain. After extracting the statistical features, a two-stage framework of distortion classification followed by quality assessment is applied. Experimental results show that the authors’ predicted quality score well matches human perceptual image quality. The proposed model outperforms state-of-the-art general-purpose NR-IQA approaches when it is tested on the LIVE database.

Inspec keywords: natural scenes; image classification; image matching; feature extraction; statistical analysis

Other keywords: general-purpose no-reference image quality assessment model; image patch coefficient statistics; distortion classification; LIVE database; hybrid NSS feature extraction; hybrid natural scene statistics; contrast normalised coefficient; two-stage framework; HNSS feature extraction; human perceptual image quality matching; general-purpose NR-IQA model; codebook extraction; spatial domain; codebook space

Subjects: Image recognition; Graphics techniques; Other topics in statistics; Other topics in statistics; Computer vision and image processing techniques

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