Paper Info
Title
A Spatial and Geometry Feature-Based Quality Assessment Model for the Light Field Images
Abstract
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.
Year
DOI
Venue
2022
10.1109/TIP.2022.3175619
IEEE TRANSACTIONS ON IMAGE PROCESSING
Keywords
DocType
Volume
Feature extraction, Geometry, Visualization, Transforms, Distortion measurement, Image quality, Degradation, Light field image, image quality assessment, sub-aperture image, contourlet transform, 3D-Gabor filter
Journal
31
Issue
ISSN
Citations 
1
1057-7149
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Hailiang Huang101.01
Huanqiang Zeng239536.94
Junhui Hou339549.84
Jing Chen48810.64
Jianqing Zhu502.03
Kai-Kuang Ma62309180.29