Fast CU Spliting Algorithms for Virtual Reality Video Based on KNN

Abstract

The coding framework for virtual reality video at present is first projecting 3D data to 2D format, then encoding it by traditional coding tools, which has much high computational complexity. In order to reduce the coding complexity based on the quality evaluation standard of virtual reality video, this paper presents a fast algorithm to speed up the Coding Unit (CU) partitioning by predicting the maximum depth of LCU with KNN classifier. Experimental results show that the proposed fast algorithm provides an average time reduction rate of 37.9% compared to the reference HM-16.16+360lib4.0, with only 1.31% BD-rate increase.