Fast CU Partitioning Algorithm for HEVC Using an Online-Learning-Based Bayesian Decision Rule

Abstract

Abstract-High Efficiency Video Coding (HEVC) is the state-of-the-art video coding standard. It adopts a hierarchical quad-tree based coding unit (CU) partitioning structure that is flexible in various texture and motion characteristics of a video signal. However, the exhaustive partitioning process for finding optimal CU partitions requires a dramatic increase in computational complexity of HEVC encoder compared with previous video coding standards. In this paper, a fast CU partitioning algorithm is proposed for HEVC encoder, which early terminates the CU partitioning process based on the Bayesian decision rule using joint online and offline learning. An online learning method is first presented based on the minimum error Bayesian decision rule using a training picture selection method with scene change detection. Next, a joint online and offline learning method is presented, which additionally trains the loss of decision making of the proposed method based on the minimum risk Bayesian decision rule. The proposed method is implemented on HEVC test software 15.0. Experimental results show that the proposed method reduces the computational complexity of HEVC encoder to 53.6% on the average with 0.71% acceptable BD rate loss in random access configuration. For other configurations, 48.4%, 48.5%, and 54.2% encoding time savings are obtained on the average for low delay, low delay-P, and all intra configurations, respectively.

Fast CU Spliting Algorithms for Virtual Reality Video Based on KNN

“…The [5] uses weighted SVM to predict CU premature termination to optimize computation complexity. In [6], a Bayesian decision rule based early termination method was reported, in which on-line learning and off-line learning were jointly applied to generate model parameters of classifiers.…”

“…In [7], a fast CU partitioning algorithm is proposed for HEVC encoder, which early on terminates the CU partitioning process based on the Bayesian decision rule using joint online and offline learning. In [8], author proposed a fast and efficient mode decision algorithm based on the Newman-Pearson rule, which consists of early SKIP mode decision and fast CU size decision.…”

Efficient AMP decision and search range adjustment algorithm for HEVC

“…A fast CU partitioning algorithm is provided in Ref. [22] for HEVC, and this method early terminates the CU size decision process based on the Bayesian decision rule. A fast interprediction mode decision algorithm is proposed in our previous work [23] that uses current block edge information to simplify RDO process so as to accelerate coding time for HEVC encoder.…”

Fast CU Size Decision for HEVC Intra Coding by Using Local Characteristics and RD Costs

“…When a current CU is not determined to early-split, the Early Termination Decision (ETD) with Bayesian decision rule is performed. In conventional algorithm ( [4], [7], and [9]), they apply the Bayesian decision for fast CU size decision using full RD cost. The local complexity of image, however, should be considered because image can have locally high RD costs.…”