Reduced-Reference 3D Image Quality Measurement via Spatial to Gradient Domain Feature Aggregation

• Published in: Journal of electrical engineering & technology Vol. 17; no. 2; pp. 1389 - 1405
• Main Authors: Ma, Jian, Xu, Guoming, Han, Xiyu
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.03.2022; 대한전기학회
• Subjects: Electrical Engineering; Electrical Machines and Networks; Electronics and Microelectronics; Engineering; Instrumentation; Original Article; Power Electronics; 전기공학; Generalized Gaussian density (GGD); Phase congruency (PC) and Human visual; Enhanced gradient magnitudes; 3D image quality
• ISSN: 1975-0102; 2093-7423
• DOI: 10.1007/s42835-021-00953-9

Objective quality measurement of a three-dimensional (3D) image is a challenging issue in various 3D visual applications since it is influenced by multiple aspects such as binocular fusion, binocular rivalry and visual comfort, etc. Existing studies show that classic 2D and some 3D image quality measurement (IQM) are only perform well for symmetric distorted 3D images, but not able to evaluate the quality of asymmetrical distorted 3D images accurately. In this paper, based on the statistical characteristics of natural images and perceptual properties of human visual system (HVS), we propose a novel reduced-reference (RR) 3D quality assessment evaluator (R3DQAE) to deal with the characteristics of 3D images. Two key technical steps are involved in R3DQAE: the statistical characteristics of 3D images and the perceptual properties of HVS. Specifically, in spatial domain, the generalized Gaussian density fits of luminance wavelet coefficients and correlations of luminance and disparity wavelet coefficients are used to represent the statistical characteristics of 3D image. Furthermore, in gradient domain, the enhanced gradient magnitudes are computed by using neighborhood phase congruency information to weight the gradient magnitudes in a locally adaptive manner. Afterward, the entropy differencing of discrete wavelet transform coefficients of enhanced gradient magnitudes are extracted as the perceptual features of HVS. Finally, the qualities index of both the statistical characteristics of 3D image and the perceptual properties of HVS are combined to yield 3D image quality index. Experiments are performed on published 3D image quality assessment database show that the proposed model achieves highly competitive performance as compared with the state-of-the art some typical full-reference and RR 3DIQM models.