Name
Title | ||
|---|---|---|
No-Reference Quality Evaluation of Stereoscopic Video Based on Spatio-Temporal Texture |
Abstract | ||
|---|---|---|
Due to the wide application of stereoscopic display technology, stereoscopic video quality assessment (SVQA) is facing great challenges, but worthwhile. Stereoscopic videos contain a great deal of information, which involves not only the spatial domain but also the spatio-temporal domain. Motion in stereoscopic video plays a critical role in quality perception, while the existing SVQA methods rarely refer to motion factors, and the performance of these methods is restrained. In this article, a novel SVQA based on motion perception is introduced and its performance is superior to that of existing excellent methods. Particularly, to appropriately reduce the amount of data processing, we extract the key-frame sequences according to the influence of movement intensity on binocular visual quality perception. The binocular summation and difference operations are implemented on extracted sequences, and then spatial texture and spatio-temporal texture statistic measurement are extracted simultaneously with local binary patterns from three orthogonal planes (LBP-TOP). Experiments are implemented on two publicly available databases and the results demonstrate the effectiveness and robustness of our algorithm for various categories of distortion stereoscopic video pairs. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TMM.2019.2961209 | IEEE Transactions on Multimedia |
Keywords | DocType | Volume |
Stereo image processing,Feature extraction,Distortion,Visualization,Quality assessment,Video recording,Data mining | Journal | 22 |
Issue | ISSN | Citations |
10 | 1520-9210 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |