Name
Abstract | ||
|---|---|---|
Research on video quality assessment (VQA) plays a crucial role in improving the efficiency of video coding and the performance of video processing. It is well acknowledged that the motion energy model generates motion energy responses in a middle temporal area by simulating the receptive field of neurons in V1 for the motion perception of the human visual system. Motivated by the biological evidence for the visual motion perception, a VQA method is proposed in this paper, which comprises the motion perception quality index and the spatial index. To be more specific, the motion energy model is applied to evaluate the temporal distortion severity of each frequency component generated from the difference of Gaussian filter bank, which produces the motion perception quality index, and the gradient similarity measure is used to evaluate the spatial distortion of the video sequence to get the spatial quality index. The experimental results of the LIVE, CSIQ, and IVP video databases demonstrate that the random forests regression technique trained by the generated quality indices is highly correspondent to human visual perception and has many significant improvements than comparable well- performing methods. The proposed method has higher consistency with subjective perception and higher generalization capability. (C) 2016 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1117/1.JEI.25.6.061613 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
video quality assessment,human visual system,motion energy model,difference of Gaussian decomposition,random forests | Computer vision,Video processing,Quarter-pixel motion,Pattern recognition,Computer science,Motion perception,Human visual system model,Motion compensation,Subjective video quality,Artificial intelligence,Video quality,Rate–distortion optimization | Journal |
Volume | Issue | ISSN |
25 | 6 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meiling He | 1 | 0 | 0.34 |
| Gangyi Jiang | 2 | 865 | 105.98 |
| Mei Yu | 3 | 542 | 86.20 |
| Yang Song | 4 | 64 | 22.68 |
| Zongju Peng | 5 | 276 | 57.69 |
| Feng Shao | 6 | 603 | 72.75 |