Abstract
The quality prediction of stereo images has great challenges without reference images. In this paper, we propose a novel no-reference stereo image quality assessment (NR-SIQA) model based on binocular visual characteristics and depth perception, which can effectively evaluate the quality of symmetric distortion and asymmetric distortion images. To be specific, we discriminate the different binocular behaviors by analyzing binocular visual characteristics, and construct the corresponding cyclopean view instead of single cyclopean view to simulate different binocular behaviors. Then, we extract monocular and binocular visual features from the left view, the right view and the synthetic cyclopean view. Furthermore, in order to evaluate the depth quality of the stereo image accurately, we extract the depth perception features from the weighted disparity map and the longitudinal correlation coefficient map. Finally, we construct the mapping relationship model from quality perception feature domain to quality score domain by training an adaptive enhancement algorithm based on support vector regression (SVR). We evaluate the performance of the proposed algorithm on four stereo image databases. The experimental results show that compared with the state-of-the-art full reference(FR), reduced reference(RR) and NR-SIQA algorithms, the proposed algorithm achieves highly competitive performance for both symmetric and asymmetric distortions.
Highlights
With the rapid development of 3D technology, 3D movies and television have played an important role in daily life and attracted attention from all over the world
To 2D-image quality assessment (IQA) metrics, 3D-IQA metrics can be classified into three categories according to the availability of the pristine reference image: the FR-SIQA methods [3]–[7] which use the original undistorted image as a reference to evaluate the quality of the stereopair; The RR-SIQA methods [8]–[10] which only utilize part of the pristine image when evaluating the quality of the image; and the no-reference stereo image quality assessment (NR-SIQA) methods [11]–[17] which evaluate the quality of the image without any reference information
To solve the above problems, we propose a blind stereo image quality evaluation model based on binocular visual characteristics and depth perception
Summary
With the rapid development of 3D technology, 3D movies and television have played an important role in daily life and attracted attention from all over the world. In the process of acquisition, compression, transmission and storage of 3D images, the left and right views may introduce different degrees and types of distortion, which affects the visual quality of experience. There are many image processing methods, such as image denoising [1] and deblurring [2], which can improve the image quality. Image quality assessment (IQA) plays an important role in image processing, because IQA can evaluate whether the method can improve the image quality. Compared with 2D images, there is certain disparity between the left and right views of a stereopair, which can provide additional depth perception. Too large disparity causes an uncomfortable experience for HVS, which affects human eyes’ judgment of image quality. Compared with the first two categories, NR-SIQA method is more difficult, but it has wider application value in practical life
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