Probability-based depth intra-mode skipping strategy and novel VSO metric for DMM decision in 3D-HEVC

Author(s)
Author(s)
Zhang, H.-B.
Fu, C.-H.
Chan, Y.-L.
Tsang, S.-H.
Date Issued
2018
Publisher
IEEE
Journal
IEEE Transactions on Circuits and Systems for Video Technology
Volume
28
Issue
2
Start page
513
End page
527
Abstract
Multiview video plus depth format has been adopted as the emerging 3D video representation recently. It includes a limited number of textures and depth maps to synthesize additional virtual views. Since the quality of depth maps influences the view synthesis process, their sharp edges should be well preserved to avoid mixing foreground with background. To address this issue, 3D-High Efficiency Video Coding (HEVC) introduces new coding tools, a partition-based intra mode [depth modeling mode (DMM)], a residual description technique [segmentwise depth coding (SDC)], and a more complex rate-distortion (RD) evaluation with view synthesis optimization (VSO), to provide more accurate predictions and achieve higher compression rate. However, these new techniques introduce a lot of possible candidates, and each of them requires complicated RD calculation in the process of intra-mode decision. They lead to unacceptable computational burden in a 3D-HEVC encoder. Therefore, in this paper, we raise two efficient techniques for depth intra-mode decision. First, by investigating the statistical characteristics of variance distributions in the two partitions of DMM, a simple but efficient criterion based on the squared Euclidean distance of variances (SEDV) is suggested to evaluate RD costs of the DMM candidates instead of the time-consuming VSO process. Second, a probability-based early depth intra-mode decision is proposed to select only the most promising mode and make the early determination of using SDC based on the low-complexity RD cost in rough mode decision. Experimental results show that the proposed algorithm with these two new techniques provides 33%-48% time reduction with little drop in the coding performance compared with the state-of-the-art algorithms.
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