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.

H.265 also called High Efficiency Video Coding is the new futuristic international standard proposed by Joint collaboration Team on Video Coding and released in 2013 in the view of constantly increasing demand of video applications. This new standard reduces the bitrate to half as compared to its predecessor H.264 at the expense of huge amount of computational burden on the encoder. In the proposed work we focus on intraprediction phase of video encoding where 33 new angular modes are introduced in addition to DC and Planar mode in order to achieve high quality videos at higher resolutions. We have proposed the use of applied machine learning to HEVC intra prediction to accelerate angular mode decision process. The features used are also low complexity features with minimal computation so as to avoid any additional burden on the encoder. The Decision tree model built is simple yet efficient which is the requirement of the complexity reduction scenario. The proposed method achieves substantial average encoding time saving of 86.59%, with QP values 4,22,27,32 respectively with minimal loss of 0.033 of PSNR and 0.0023 loss in SSIM which makes it suitable for acceptance of High Efficiency Video coding in real time applications