Name
Title | ||
|---|---|---|
Fast synthesized and predicted just noticeable distortion maps for perceptual multiview video coding. |
Abstract | ||
|---|---|---|
The just noticeable distortion (JND) map is a useful tool for perceptual video coding. However, direct calculation of the JND map incurs high complexity, and the problem is aggravated in multiview video coding. In this paper, two fast methods are proposed to generate the JND maps of multiview videos. In the first method, the JND maps of some anchor views are used to synthesize the JND maps of other views via the depth image based rendering (DIBR), which can be much faster than direct JND computation. In the second method, the motion and disparity vectors obtained during the video coding are employed to predict the JND maps. If the prediction is not satisfactory, the JND block will be refreshed by calculating the JND directly. This method does not need any camera parameters and depth maps. The performances of the two fast JND map generation methods are evaluated in a perceptual MVC framework, where the residuals after spatial, temporal, or inter-view prediction are tuned according to the JND thresholds to save the bits without affecting the perceptual quality. Experimental results show that the JND prediction method has better accuracy and lower complexity. In addition, both fast JND methods lead to negligible degradation of the coding performance, compared to the direct JND method. (C) 2012 Elsevier Inc. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.jvcir.2012.04.004 | Journal of Visual Communication and Image Representation |
Keywords | Field | DocType |
Multiview video coding,Just noticeable distortion,Depth image based rendering,Motion estimation and compensation,Computational complexity,Perceptual video coding,Human visual system,Block based prediction | Just noticeable distortion,Computer vision,Pattern recognition,Human visual system model,Computer science,Multiview Video Coding,Coding (social sciences),Artificial intelligence,Image-based modeling and rendering,Perception,Computational complexity theory,Computation | Journal |
Volume | Issue | ISSN |
24 | 6 | 1047-3203 |
Citations | PageRank | References |
1 | 0.36 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Gao | 1 | 7 | 1.83 |
| Xiaoyu Xiu | 2 | 94 | 12.99 |
| Jie Liang | 3 | 707 | 80.89 |
| Weisi Lin | 4 | 5366 | 280.14 |