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With the recent development of new three-dimensional (3D) multimedia services such as 3D television or free viewpoint television, a new 3D video format, called multiview video + depth (MVD) is currently being investigated. MVD allows synthesizing as many views as required at the receiver side, thus providing smooth scene transitions and the ability to experience a new 3D perspective with each different viewing point. The format introduces, alongside traditional 2D image sequences, sequences of depth maps, which must be efficiently coded to achieve good quality for the synthesized views. One approach to code depth videos is to exploit the correlations between texture and depth. In this work, we propose a new tool to code depth videos in which the texture Intra modes are inherited and used as predictors for the depth Intra modes, hence reducing the mode signaling bitrate. The tool is only used in prediction units where texture and depth Intra directions, or modes, are expected to match. Two criteria that exploit the statistical dependency between the texture and depth Intra modes are studied in this work: GradientMax and DominantAngle. Average bitrate reductions of 1.3 and 1.6% on synthesized sequences are reported for GradientMax and DominantAngle, respectively. The latter method additionally achieves 2.3% bitrate reduction on depth sequences.
Fig. 1. Angular Intra prediction modes in HEVC.
Fig. 2. Geometric partitioning and resulting Intra direction of a texture and depth PU caused by the presence of an edge.
Fig. 3. Kendo texture frame and associated depth map analysis.
Fig. 4. Definition of the corresponding texture PU T ref for a currently coded depth PU in our algorithm.
Fig. 5. Algorithm of our proposed tool.
Fig. 6. Two types of texture PUs containing sharp edges.
Fig. 7. Module matrices obtained after Sobel filtering of two types of texture PUs containing sharp edges.
Fig. 8. Angles histograms of two types of texture PUs containing sharp edges.
Fig. 9. Texture–depth Intra mode matchings PUs and inheritance PUs with the GradientMax and DominantAngle criteria, in the first frame of the central view of PoznanHall2.
Fig. 10. First top-left 64 × 64 PU (with adjusted contrast for visibility) of the first texture frame in the central view of PoznanHall2.
Fig. 11. Average gains on SV as a function of the threshold.
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Dr. James Kirk, Dr.Sam Park
APSIPA Transactions on Signal and Information Processing / Volume 3 / 2014, e1 (13 pages)
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