A Gabor Feature-Based Quality Assessment Model for the Screen Content Images

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

25 Scopus Citations
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Author(s)

  • Huanqiang Zeng
  • Lin Ma
  • Jing Chen
  • Kai Kuang Ma

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)4516-4528
Journal / PublicationIEEE Transactions on Image Processing
Volume27
Issue number9
Online published23 May 2018
Publication statusPublished - Sep 2018

Abstract

In this paper, an accurate and efficient full-reference image quality assessment (IQA) model using the extracted Gabor features, called Gabor feature-based model (GFM), is proposed for conducting objective evaluation of screen content images (SCIs). It is well-known that the Gabor filters are highly consistent with the response of the human visual system (HVS), and the HVS is highly sensitive to the edge information. Based on these facts, the imaginary part of the Gabor filter that has odd symmetry and yields edge detection is exploited to the luminance of the reference and distorted SCI for extracting their Gabor features, respectively. The local similarities of the extracted Gabor features and two chrominance components, recorded in the LMN color space, are then measured independently. Finally, the Gabor-feature pooling strategy is employed to combine these measurements and generate the final evaluation score. Experimental simulation results obtained from two large SCI databases have shown that the proposed GFM model not only yields a higher consistency with the human perception on the assessment of SCIs but also requires a lower computational complexity, compared with that of classical and state-of-the-art IQA models.

Research Area(s)

  • Computational modeling, Distortion measurement, Feature extraction, Gabor feature, Image color analysis, Image edge detection, Image quality assessment (IQA), Quality assessment, screen content images (SCIs), Visualization

Citation Format(s)

A Gabor Feature-Based Quality Assessment Model for the Screen Content Images. / Ni, Zhangkai; Zeng, Huanqiang; Ma, Lin; Hou, Junhui; Chen, Jing; Ma, Kai Kuang.

In: IEEE Transactions on Image Processing, Vol. 27, No. 9, 09.2018, p. 4516-4528.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal