Adaptive Hierarchical Motion Estimation Optimization for Scalable HEVC

The scalable extension of the HEVC Video Coding Standard (H. 265) offers elaborate mechanisms for motion vector prediction and estimation. SHEVC builds on the standard by extending predictor lists for Coding Unit blocks, utilizing base-layer information in the inference of enhancement-layer Coding Units. The complex, exhaustive search schemes in use can be aided by hierarchical optimizations in subpixel motion estimation, which we propose for slow-moving CUs per frame. In this paper we implement and test an adaptive optimization of motion estimation in the standard (SHM 6.1 software release), based on a statistical analysis of the behavior of subpixel motion vector differentials in each spatial mode per Coding Unit. We propose that the least granular mode (64x64 PEL macro-block in current release) contains sufficient information at subpixel levels to decide best-mode selection, i.e., whether a complete recursion through the inner partitions (higher granularity) is required in the estimation of a CU motion vector. We further propose that subpixel motion estimation overheads can be avoided below a set threshold, given conditions set in base and enhancement layer motion estimation for priorly computed modes in the same CU. Both optimization methods are tested across a diverse set of video sequences, producing negligible quality penalties at for a sizable reduction in encoding time.