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Due to the deficient knowledge of binocular vision properties, how to effectively evaluate stereoscopic images still remains a challenging task. Inspired by multichannel processing of human visual system (HVS), we propose a blind method for stereoscopic image quality assessment (SIQA) by extracting quality related features in sub-bands of the image. First of all, we introduce the shearlet transform to decompose the left- and right-view images into multiple sub-bands content with diverse combinations of scales and orientations, and obtain the combined view based on energy-weighted summation of the corresponding sub-bands of two eye views. Then, natural scene statistics (NSS) of the original left and right images are obtained as quality-sensitive features, followed by extracting NSS features of the sub-bands of left, right and combined views. Moreover, we calculate the gradient similarity between each sub-band pair to denote the asymmetric distortion and disparity information. Finally, all the extracted features are mapped into a quality score by support vector regression (SVR). experimental results on multiple benchmark databases verify the superiority of our method.

We develop a new no-reference (NR) stereoscopic video quality assessment model that adopts oriented local gravitational force (OLGF) statistics in the space-time domain. The OLGF is a novel extension of an existing local gravitational force descriptor and includes two new components: relative local gravitational force magnitude and relative local gravitational force orientation. The resulting algorithm, called Stereoscopic Video Integrity Predictor using OLGF Statistics (SVIPOS), first uses our previous work to generate a cyclopean image and a product image in spatial domain from a stereoscopic video sequence pair, and calculates a frame difference image in temporal domain from a left video sequence. Then the OLGF model is deployed to compute various local gravitational force responses on the cyclopean image, the product image and the frame difference image. Finally, a support vector regression is used to map statistical features extracted from the computed gravitational force responses to stereoscopic video quality predications. SVIPOS is thoroughly tested on three publicly stereoscopic video quality databases. The experimental results show that SVIPOS outperforms state-of-the-art stereoscopic video quality methods.

Perceptual stereo image quality assessment (SIQA) aims to design computational models to measure the stereo image quality in accordance with human opinions. In this paper, a novel reduced-reference (RR) SIQA is proposed by characterizing the statistical and perceptual properties of the stereo image in both the spatial and gradient domains. To be specific, in the spatial domain, we extract the parameters of the generalized Gaussian distribution fits of luminance wavelet coefficients to form the underlying features. In the gradient domain, the modified gradient magnitudes maps are generated by jointly considering human visual system's contrast sensitivity and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner. Afterward, perceptual features are extracted based on the entropy of discrete wavelet transform coefficients of modified gradient magnitudes. Furthermore, we consolidate the left and right features into a single set of features per stereo image pair. Finally, the qualities of both the spatial and gradient domains are combined to obtain the overall quality of stereo image. Extensive experiments performed on popular data sets demonstrate that the proposed RR-SIQA method achieves highly competitive performance as compared with the state-of-the-art RR-SIQA models as well as full-reference ones for both symmetric and asymmetric distortions.

Image quality assessment (IQA) technology is facing a new challenge due to the large amount of demand for high-quality 3-D-synthesized views stimulated by the rapid development of virtual reality applications. Since 3-D synthesis views commonly rely on the depth-image-based rendering technology to synthesize virtual images (without reference images in reality and containing geometric distortion), only the no-reference (NR) quality assessment method can meet the requirements. However, most of the current IQA methods for 3-D-synthesized views are full-reference methods. So far, only one specialized NR IQA method for 3-D-synthesized views has been proposed, but its computation is too expensive. For this reason, we have previously proposed a method for extracting geometric distortion regions using the Joint Photographic Experts Group (JPEG) image compression technology to evaluate image quality. In this paper, we consider that although heavy JPEG compression can effectively extract the geometric distortion area, it will ignore the image quality degradation caused by other distortions. Therefore, we have improved our previous work to extract geometric distortions and non-geometric distortions by high-level JPEG compression and lowlevel JPEG compression, respectively. The overall image quality score was obtained by the fusion of the two results. Experiments indicate that the proposed blind quality model is superior to modern full-, reduced-, and no-reference methods. Compared with our previous work, the performance of the new algorithm has been greatly improved. At the same time, compared with the existing dedicated NR IQA method, the performance is similar but the calculation speed has obvious advantages.