Complexity Estimation for Load Balancing of 360-Degree Intra Versatile Video Coding

The ever increasing demand for image and video content poses new requirements to support higher resolutions and richer representation formats, creating new challenges in coding algorithms. The forthcoming Versatile Video Coding (VVC) standard aims to increase the coding efficiency of existing algorithms and it is particularly suitable for Ultra-High Definition (UHD) resolutions and 360° video. However, since coding efficiency gains are obtained at the cost of increased complexity, fast computational approaches are needed to cope with realtime requirements, such as parallel processing. Thus, this work presents a contribution towards efficient parallel encoding of 360° video, based on coding complexity estimation and nonuniform data-level splitting (slice-based) for load balancing across multiple processors. A machine learning approach is proposed to estimate the complexity of intra coding VVC, using uncorrelated features, obtained through Principal Component Analysis (PCA) and Extremely Randomised Trees (ERT). Then, a complexity-balanced slice partition is devised, taking advantage of the clustered complexity inherent to Equirectangular Projection (ERP). It is shown that coding complexity is estimated with an accuracy of 92.25%, and the encoding time is reduced by 8.50%, when compared to the case where the 360° frames are evenly split.