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Content Provider | IET Digital Library |
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Author | Jiang, Gangyi He, Meiling Yu, Mei Shao, Feng Peng, Zongju |
Abstract | Perceptual quality assessment of stereoscopic images is a challenge in three-dimensional video systems. Existing studies suggest that simply averaging the quality of left and right views can effectively predict the quality of symmetrically distorted stereoscopic images, but prediction deviation occurs in the case of asymmetrically distorted stereoscopic images. Most previous stereoscopic image quality assessment (SIQA) methods have been based only on the luminance component of the images; in addition, the basis of human visual perception is critical to image quality assessment and lies on the low-dimensional manifold. Inspired by this, a new perceptual SIQA method is proposed, which includes two stages: training stage and quality prediction stage. In the training stage, the authors apply Tucker decomposition to RGB images to reduce dimensions along colour channels to produce training sets, and the projection matrix is obtained through manifold learning. In the quality prediction stage, considering the binocular visual characteristics of visual perception, the overall stereoscopic estimate depends on the monocular image quality via a local energy ratio based pooling strategy and cyclopean based binocular quality. Extensive experiments on three available benchmark databases demonstrate that the proposed metric has better performance and achieves highly consistent alignment with subjective assessment compared with state-of-the-art SIQA metrics. |
Starting Page | 810 |
Ending Page | 818 |
Page Count | 9 |
ISSN | 17519659 |
Volume Number | 12 |
e-ISSN | 17519667 |
Issue Number | Issue 5, May (2018) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/12/5 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0650 |
Journal | IET Image Processing |
Publisher Date | 2018-01-03 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Algebra Asymmetrically Distorted Stereoscopic Image Benchmark Databases Binocular Visual Characteristic Colour Channel Computer Vision And Image Processing Technique Cyclopean Based Binocular Quality Dimension Reduction Human Visual Perception Image Colour Analysis Knowledge Engineering Technique Learning in AI Left View Quality Local Energy Ratio Based Pooling Strategy Low-dimensional Manifold Manifold Learning Matrix Algebra Monocular Image Quality Optical, Image And Video Signal Processing Overall Stereoscopic Estimate Perceptual SIQA Method Perceptual Stereoscopic Image Quality Assessment Method Prediction Deviation Projection Matrix Quality Prediction Stage RGB Image Right View Quality Stereo Image Processing Tensor Decomposition Tensors Training Sets Training Stage Tucker Decomposition Visual Perception |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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