No-reference Stereoscopic Video Quality Assessment Using Spatial, Temporal, Depth, Transform, and Spatiotemporal Features
| Autor: | LIN, MEI-HSIU, 林美秀 |
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| Rok vydání: | 2017 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 Recently, 3D technology application is more and more widespread. Thus, humans pay more attention on stereoscopic video quality. In other words, the stereoscopic video quality assessment approaches will be widely used. No-reference stereoscopic video quality assessment technology is the most useful and effective way. Hence, no-reference stereoscopic video quality assessment technology is mainly focused in this study. First, five domain features including spatial, temporal, depth, transform, and spatiotemporal features are extracted. On the spatial domain, blurriness, blockiness, local binary pattern (LBP), and edge information are extracted. On the temporal domain, variation information of luminance, DC values, and disparity are extracted. On the depth domain, disparity and depth motion are extracted. On the transform domain, discrete wavelet transform (DWT) and discrete cosine transform (DCT) information are extracted. On the spatiotemporal domain, histogram of gradient (HOG) and 3D-DCT information are extracted. Each feature vector is obtained by using histogram statistics and normalization to the same distribution. Then, the feature vectors from the left-view and right-view videos are averaged. Here, feature selection is applied to strike out the unnecessary features to improve the performance. Here, support vector regression (SVR) is applied to estimate the stereoscopic video quality score. Finally, experimental results show that the proposed approach is better than the other NR approach on NAMA3DS1_COSPAD1 database and compares favorably with others FR and RR approaches. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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