This paper proposes a complexity reduction algorithm for the depth maps intra prediction of the emerging 3D High Efficiency Video Coding standard (3D-HEVC). The 3D-HEVC introduces a new set of specific tools for the depth map coding that includes four Depth Modeling Modes (DMM) and these new features have inserted extra effort on the intra prediction. This extra effort is undesired and contributes to increasing the power consumption, which is a huge problem especially for embedded-systems. For this reason, this paper proposes a complexity reduction algorithm for the DMM 1, called Gradient-Based Mode One Filter (GMOF). This algorithm applies a filter to the borders of the encoded block and determines the best positions to evaluate the DMM 1, reducing the computational effort of DMM 1 process. Experimental analysis showed that GMOF is capable to achieve, in average, a complexity reduction of 9.8% on depth maps prediction, when evaluating under Common Test Conditions (CTC), with minor impacts on the quality of the synthesized views.
This paper presents a fast depth map encoding for 3D-High Efficiency Video Coding (3D-HEVC) based on static decision trees. We used data mining and machine learning to correlate the encoder context attributes, building the static decision trees. Each decision tree defines that a depth map Coding Unit (CU) must be or not be split into smaller blocks, considering the encoding context through the evaluation of the encoder attributes. Specialized decision trees for I-frames, P-frames and B-frames define the partitioning of 64 × 64, 32 × 32, and 16 × 16 CUs. We trained the decision trees using data extracted from the 3D-HEVC Test Model considering all-intra and random-access configurations, and we evaluated the proposed approach considering the common test conditions. The experimental results demonstrated that this approach can halve the 3D-HEVC encoder computational effort with less than 0.24% of BD-rate increase on the average for all-intra configuration. When running on random-access configuration, our solution is able to reduce up to 58% the complete 3D-HEVC encoder computational effort with a BD-rate drop of only 0.13%. These results surpass all related works regarding computational effort reduction and BD-rate.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.