Abstract
Fast CU Spliting Algorithms for Virtual Reality Video Based on KNN
Highlights
Virtual reality video is a special kind of video representing the whole scene of the environment in 360 degree
In [7], a fast Coding Unit (CU) partitioning algorithm is proposed for High Efficiency Video Coding (HEVC) encoder, which early on terminates the CU partitioning process based on the Bayesian decision rule using joint online and offline learning
In [9], author proposed a machine learning-based fast coding unit (CU) depth decision method for High Efficiency Video Coding (HEVC), which optimizes the complexity allocation at CU level with given rate-distortion (RD) cost constraints.[10] proposes a fast CU splitting algorithm which can narrow CU depth range and early terminate the CU splitting based on III
Summary
Virtual reality video is a special kind of video representing the whole scene of the environment in 360 degree. The coding and transmission of virtual reality video mainly relies on projection every frame of the 3D style data into 2D one, and using traditional coding framework such as HEVC, H.264 to fulfill encoding. When 8x8 CUs’ calculating is complete, encoder compare the sum of four 8×8 CUs’ RD-Costs to the RD-Cost corresponding to the 16×16 CU’s to decide whether to choose the four 8×8 CUs or the a 16×16 CU. The virtual reality video is encoded by HEVC after projection, its quality evaluation standard is different from HEVC. If the RD-cost of the current CU is larger than the RD-cost of the parent CU, there is no need to divide it, and the current CU as a whole These comparisons occur after the end of all CU traversals of different sizes, which means much high computation burden. We use KNN to predict the maximum depth of LCU, and to reduce the redundancy of LCU partitioning operation
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