This paper presents view synthesis optimization for 3D-HEVC based on a new texture smoothness process. In the original method, all pixels are exhaustively rendered to get distortions from synthesized views. Since not ...
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ISBN:
(纸本)9781467369985
This paper presents view synthesis optimization for 3D-HEVC based on a new texture smoothness process. In the original method, all pixels are exhaustively rendered to get distortions from synthesized views. Since not all pixels from the distorted depth map may cause distortions in the synthesized view, it brings unnecessary coding complexity. In this paper, lines of pixels are skipped based on the analysis of pixel regularity from smooth texture regions. It is due to the fact that the distorted disparity may not have much effect on the synthesized view in smooth texture regions. The proposed method can reduce the coding complexity of view synthesis optimization without significant performance loss.
The uniform intra prediction increases the intra prediction modes up to 35 and brings better coding efficiency in HEVC. Besides, depth modelling modes (DMMs) are introduced in depth intra coding of 3D-HEVC to preserve...
详细信息
ISBN:
(纸本)9781479983407
The uniform intra prediction increases the intra prediction modes up to 35 and brings better coding efficiency in HEVC. Besides, depth modelling modes (DMMs) are introduced in depth intra coding of 3D-HEVC to preserve sharp edges and avoid ringing artifacts in a synthesized view. Meanwhile, the encoding time of depth intra coding rapidly increases due to a huge number of intra mode candidates. Based on the spatial correlation of a depth map, we find that not all of the intra modes are necessary to be considered in most cases. Hence, a fast content-dependent depth intra mode decision algorithm is raised in this paper by classifying the spatial distribution of the reference pixels. Simulation results show that the proposed adaptive fast algorithm can save 21%-35% time of the depth coding with the insignificant bit rate increase compared with the state-of-the-art algorithm.
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