Adaptive QP offset selection algorithm for virtual reality 360-degree video based on CTU complexity

Virtual reality 360-degree video requires ultra-high resolution to provide realistic feeling and dynamic perspective. Huge data volume brings new challenges to coding and transmission. Quantization parameter (QP) is one of the key parameters to control output bitrate and reconstruction quality during coding process. Many QP offset selection algorithms designed for this kind of video are based on latitude or Equirectangular Projection (ERP) weight maps, which cannot adapt to the situation of the flat block in tropical area or the complex block in polar area. In this paper, a new metric to measure complexity of Coding Tree Unit (CTU) is designed, and an adaptive QP offset selection algorithm is proposed based on CTU complexity to improve the quantization process. Each CTU is classified into one of the five categories according to its complexity, and then different QP offset value is determined for each category. By improving the quality of the visually sensitive area and reducing the bitrate of the flat one, the efficiency of the encoder is improved. The experimental results show that, compared with the HM16.20, the WS-PSNR increases by 0.40 dB, the BD-rate reduces by 1.99%, and the quality of visually sensitive areas has improved significantly.