Gorley P, Holliman N (2008) Stereoscopic image quality metrics and compression. Yasakethu SLP, Hewage CTER, Fernando WAC, Kondoz AM (2008) Quality analysis for 3D video using 2D video quality models. Advances and future trends, visual signal quality assessment. Su CC, Moorthy AK, Bovik AC (2015) Visual quality assessment of stereoscopic image and video: challenges. Niu YZ, Zhong YN, Guo WZ, Shi YQ, Chen PK (2019) 2D and 3D image quality assessment: a survey of metrics and challenges. Shao F, Tian WJ, Lin WS, Jiang GY, Dai QH (2017) Learning sparse representation for no-reference quality assessment of multiply distorted stereoscopic images. Karimi M, Nejati M, Soroushmehr SMR, Samavi S, Karimi N, Najarian K (2017) Blind stereo quality assessment based on learned features from binocular combined images. Zhou WJ, Yu L, Wu MW (2015) Simulating binocular vision for no-reference 3D visual quality measurement. 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. 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. ![]() Afterward, the entropy differencing of discrete wavelet transform coefficients of enhanced gradient magnitudes are extracted as the perceptual features of HVS. 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. 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. Two key technical steps are involved in R3DQAE: the statistical characteristics of 3D images and the perceptual properties of HVS. 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. 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. 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.
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