A Principled Approach to Detecting Surprising Events in Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Motion tuned spatio-temporal quality assessment of natural videos
IEEE Transactions on Image Processing
Study of subjective and objective quality assessment of video
IEEE Transactions on Image Processing
A motion compensated approach to video quality assessment
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Spacetime Texture Representation and Recognition Based on a Spatiotemporal Orientation Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Most existing video quality metrics measure temporal distortions based on optical-flow estimation, which typically has limited descriptive power of visual dynamics and low efficiency. This paper presents a unified and efficient framework to measure temporal distortions based on a spacetime texture representation of motion. We first propose an effective motion-tuning scheme to capture temporal distortions along motion trajectories by exploiting the distributive characteristic of the spacetime texture. Then we reuse the motion descriptors to build a self-information based spatiotemporal saliency model to guide the spatial pooling. At last, a comprehensive quality metric is developed by combining the temporal distortion measure with spatial distortion measure. Our method demonstrates high efficiency and excellent correlation with the human perception of video quality.