Fast 3D-HEVC Depth Map Encoding Using Machine Learning

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 <inline-formula> <tex-math notation="LaTeX">$64\times 64$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$32\times 32$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula> 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.