Abstract
With the wide application of stereoscopic video technology, the quality of stereoscopic video has attracted people’s attention. Objective stereoscopic video quality assessment (SVQA) is highly challenging, but essential, particularly the no-reference (NR) SVQA method, where reference information is not needed and a large number of samples are required for training and testing sets. However, as far as we know, there are only a few samples in the established stereo video database, which is unsuitable for NR quality assessment and seriously hampers the development of NR-SVQA method. For these difficulties that we encountered, we carry out a comprehensive subjective evaluation of stereoscopic video quality in our newly established TJU-SVQA databases that contain various contents, mixed resolution coding and symmetrically/asymmetrically distorted stereoscopic videos. Furthermore, we propose a new inter-frame cross map to predict the objective quality scores. We compare and analyze the performance of several state-of-the-art 2D and 3D quality evaluation methods on our new databases. The experimental results on our established databases and a public database demonstrate that the proposed method can robustly predict the quality of stereoscopic videos.
Highlights
W ITH the rapid development of 3D video technology, the demand for 3D contents can be expected to become increasing in the near future
Subjective quality evaluation is to ask a sufficiently great number of subjects to assess the degree of distortion of image/video according to their viewing experience
The data of Pearson linear correlation coefficient (PLCC) and Spearman rank correlation coefficient (SROCC) associated with each subject was obtained, and the average values of PLCC and SROCC are shown in Table V, where the high average indicator present a good consistency between individual score and mean opinion score (MOS)
Summary
W ITH the rapid development of 3D video technology, the demand for 3D contents can be expected to become increasing in the near future. Subjective quality evaluation is to ask a sufficiently great number of subjects to assess the degree of distortion of image/video according to their viewing experience. Classical objective quality schemes, such as peak signal to noise ratio (PSNR) and structural similarity (SSIM) [10] are FR methods, but their results can not be consistent with subjective evaluation. To overcome this difficulty, new objective algorithms with stereo vision are being exploited. 2) We present a binocular characteristic inspired model, which leads to a markedly improved NR-SVQA model This method uses a new infer-frame cross to effectively reflect the relationship between video distortion degree and the MOS, and increases efficiency.
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