A Spatial and Geometry Feature-Based Quality Assessment Model for the Light Field Images

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

2 Scopus Citations
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  • Hailiang Huang
  • Huanqiang Zeng
  • Jing Chen
  • Jianqing Zhu
  • Kai-kuang Ma


Original languageEnglish
Journal / PublicationIEEE Transactions on Image Processing
Online published23 May 2022
Publication statusOnline published - 23 May 2022


This paper proposes a new full-reference image quality assessment (IQA) model for performing perceptual quality evaluation on light field (LF) images, called the spatial and geometry feature-based model (SGFM). Considering that the LF image describe both spatial and geometry information of the scene, the spatial features are extracted over the sub-aperture images (SAIs) by using contourlet transform and then exploited to reflect the spatial quality degradation of the LF images, while the geometry features are extracted across the adjacent SAIs based on 3D-Gabor filter and then explored to describe the viewing consistency loss of the LF images. These schemes are motivated and designed based on the fact that the human eyes are more interested in the scale, direction, contour from the spatial perspective and viewing angle variations from the geometry perspective. These operations are applied to the reference and distorted LF images independently. The degree of similarity can be computed based on the above-measured quantities for jointly arriving at the final IQA score of the distorted LF image. Experimental results on three commonly-used LF IQA datasets show that the proposed SGFM is more in line with the quality assessment of the LF images perceived by the human visual system (HVS), compared with multiple classical and state-of-the-art IQA models.

Research Area(s)

  • 3D-Gabor filter, contourlet transform, Degradation, Distortion measurement, Feature extraction, Geometry, Image quality, image quality assessment, Light field image, sub-aperture image, Transforms, Visualization