Linear adaptive search range model for uni-prediction and motion analysis for bi-prediction in HEVC

Abstract

High Efficiency Video Coding (HEVC) is the up-to-date video coding standard. Compared to the predecessor H.264/AVC, HEVC can further reduce approximately 50% bit rate on average with the competing perceptual quality. On the other hand, experiment shows that HEVC requires more than 4 times computational complexity during the encoding procedure. In ours test, even using fast TZSearch, integer motion estimation (IME) still accounts for 20%–30% of encoding time. In this paper, we propose two adaptive search range (ASR) algorithms to address this problem in IME. First, we present an ASR algorithm based on linear adaptive search range model (LAM-ASR) for uni-prediction. This model considers the impacts of the motion consistency, PU size and the amplitude of motion vector predictor (MVP). In order to offer more flexibility, we introduce a scale factor to this model. Second, for bi-prediction, we propose another ASR algorithm based on motion analysis (MA-ASR), which assigns different search range to PU by making full use of the motion information obtained from uni-prediction. Experimental results show that when embedded into the fast TZSearch method of the reference software, the two proposed ASR algorithms can averagely save 42.0% of the IME time with 0.023dB BD-PSNR degradation or equally 0.7% BD-BR increase.