Objective Quality Assessment Of Stereoscopic Images Research Articles

Dual-Stream Interactive Networks for No-Reference Stereoscopic Image Quality Assessment.

The goal of objective stereoscopic image quality assessment (SIQA) is to predict the human perceptual quality of stereoscopic/3D images automatically and accurately. Compared with traditional 2D image quality assessment, the quality assessment of stereoscopic images is more challenging because of complex binocular vision mechanisms and multiple quality dimensions. In this paper, inspired by the hierarchical dual-stream interactive nature of the human visual system, we propose a stereoscopic image quality assessment network (StereoQA-Net) for no-reference stereoscopic image quality assessment. The proposed StereoQA-Net is an end-to-end dual-stream interactive network containing left and right view sub-networks, where the interaction of the two sub-networks exists in multiple layers. We evaluate our method on the LIVE stereoscopic image quality databases. The experimental results show that our proposed StereoQA-Net outperforms state-of-the-art algorithms on both symmetrically and asymmetrically distorted stereoscopic image pairs of various distortion types. In a more general case, the proposed StereoQA-Net can effectively predict the perceptual quality of local regions. In addition, cross-dataset experiments also demonstrate the generalization ability of our algorithm.

A new objective stereoscopic image assessment model based on stereoscopic perception

In order to establish a stereoscopic image quality assessment method which is consistent with human visual perception, we propose an objective stereoscopic image quality assessment method. It takes into account the strong correlation and high degree of structural between pixels of image. This method contains two models. One is the quality synthetic assessment of left-right view images, which is based on human visual characteristics, we use the Singular Value Decomposition (SVD) that can represent the degree of the distortion, and combine the qualities of left and right images by the characteristics of binocular superposition. The other model is stereoscopic perception quality assessment, due to strong stability of image’s singular value characteristics, we calculate the distance of the singular values and structural characteristic similarity of the absolute difference maps, and utilize the statistical value of the global error to evaluate stereoscopic perception. Finally, we combine two models to describe the stereoscopic image quality. Experimental results show that the correlation coefficients of the proposed assessment method and the human subjective perception are above 0.93, and the mean square errors are all less than 6.2, under JPEG, JP2K compression, Gaussian blurring, Gaussian white noise, H.264 coding distortion, and hybrid cross distortion. It indicates that the proposed stereoscopic objective method is consistent with human visual properties and also of availability.

Objective Stereoscopic Image Quality Assessment Method for 3DV System

Stereoscopic imaging technology has aroused a great concern recently due to the increasingly wide range of stereoscopic applications. Stereoscopic image quality assessment is of vital importance to evaluate the performance of three-dimensional video (3DV) systems. However, stereoscopic image quality assessment methods are very scarce presently. In the paper, according to human visual sensitivities to different regions with different scales, an objective stereoscopic image quality assessment (OSIQA) model is proposed to predict visual quality of a stereoscopic image. The structural differences among scales and regions are firstly calculated to obtain the left-right image quality assessment (LR-IQA) index after multi-scale decomposition. Then, a concept of absolute disparity image is defined to describe absolute difference between the left and the right images, and the changes referring to the brightness, structure and pixel statistical information between the original and the distorted absolute disparity images are regarded to gain the stereoscopic perception image quality assessment (SP-IQA) index. Finally, according to the weights of LR-IQA and SP-IQA indices, LR-IQA and SP-IQA are combined into OSIQA. Experimental results show that the proposed method closely approximates subjective evaluation with the correlation coefficient of 0.938, the Spearman rank-order correlation coefficient of 0.946, and the root mean square error of 5.952 for all the five distortions, including Gaussian blurring, Gaussian white noise, JP2K, JPEG and H264 compressions.