Intelligent coding unit partitioning using predictive data mining

Abstract

Increasing applications of videos in everyday life demands compressing the videos further. International bodies for Video Coding standards are working toward making it more efficient in terms of reducing bitrate so as to efficiently compress the high-resolution videos. With increasing resolution, the size of the Coding Units increases. Latest Video Coding techniques like High Efficiency Video Coding (HEVC) and Versatile Video coding (VVC) proposed Larger coding Units with flexible Quadtree decompositions. In Inter-picture prediction all the sub blocks have to find best partitioning structure during motion estimation. Due to larger coding units finding the best partitioning introduces computational complexity. In the proposed work we present a computational complexity control scheme using predictive data mining. The method helps to predict whether to split or no split the coding unit. The decision tree model trained offline in the proposed work achieves 77.73% saving in encoding time with minimal change of 0.15 in average PSNR and 0.00074 in average SSIM values.