Abstract
High-efficiency video coding (HEVC) is a new video coding standard being developed by the Joint Collaborative Team on Video Coding. HEVC adopted numerous new tools, such as more flexible data structure representations, which include the coding unit (CU), prediction unit, and transform unit. In the partitioning of the largest coding unit (LCU) into CUs, rate distortion optimization (RDO) is applied. However, the computation complexity of RDO is too high for real-time application scenarios. Based on studies on the relationship between CUs and their entropy, this paper proposes a fast algorithm based on entropy to partition LCU as a substitute for RDO in HEVC. Experimental results show that the proposed entropy-based LCU partition algorithm can reduce coding time by 62.3% on average, with an acceptable loss of 3.82% using Bjøntegaard delta rate.
Highlights
As the generation of video coding standards, high-efficiency video coding (HEVC) [1] aims to reduce bit rate in half with the same reconstructed video quality as H.264/AVC, which is the latest-generation video coding
Whether or not a coding unit (CU) whose size is larger than smallest CU is encoded or split into four equal-sized CUs is decided by using a rate distortion optimization (RDO)
This study shows that the CUs partitioned by RDO process closely relate to the information content of each CU
Summary
As the generation of video coding standards, high-efficiency video coding (HEVC) [1] aims to reduce bit rate in half with the same reconstructed video quality as H.264/AVC, which is the latest-generation video coding. The areas with lesser information content are partitioned into larger CUs. By contrast, the areas with more information content are partitioned into smaller CUs. a larger CU can bring significant bitrate reduction, the HEVC encoder has to search for all possible CUs to obtain the optimized CUs, resulting in an extremely large computation complexity [8]. Shen [11] proposed a CU size decision algorithm, which collects relevant and computational-friendly features to assist decisions on CU splitting We propose an entropy-based fast CU-sized decision algorithm to replace the RDO used in the quadtree structure.
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