Open AccessIntelligent Coding Unit Partitioning using Predictive Data Mining
Author(s) -
Chhaya Shishir Pawar*,
Dr.Sudhir Deoraoji Sawarkar
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l2469.1081219
Subject(s) - quadtree , computer science , coding (social sciences) , coding tree unit , context adaptive binary arithmetic coding , algorithmic efficiency , computational complexity theory , predictive coding , artificial intelligence , data compression , data mining , algorithm , real time computing , decoding methods , mathematics , statistics
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.