Machine Learning-Based Coding Decision Making in H.265/HEVC CTU Division and Intra Prediction

Abstract

High efficiency video coding (HEVC) has been deemed as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. In this research project, in compliance with H.265 standard, the authors focused on improving the performance of encode/decode by optimizing the partition of prediction block in coding unit with the help of supervised machine learning. The authors used Keras library as the main tool to implement the experiments. Key parameters were tuned for the model in the convolution neuron network. The coding tree unit mode decision time produced in the model was compared with that produced in the reference software for HEVC, and it was proven to have improved significantly. The intra-picture prediction mode decision was also investigated with modified model and yielded satisfactory results.

An Adaptive CU Split Method for VVC Intra Encoding

“…With the rapid development of video applications for video resolution and storage (Peng and Cohen et al, 2020), High-Efficiency Video Coding (HEVC/H.265) will not be able to meet the needs of today's diverse demand (Mahdavi & Hamzaoglu, 2021;Yang et al, 2021;Jiang et al, 2020). In order to improve video coding technology, the Joint Video Exploration Team (JVET) developed Versatile Video Coding (VVC) (Bross et al, 2021;Acharjee & Chaudhuri et al, 2022) based on HEVC, which was finalized as a new video compression standard in July 2020 with the coding technologies and methods of HEVC retained and the new encoding tools introduced, such as Binary Tree (BT) and Trident Tree (TT) in a Multi Type Tree (MTT) structure (Chen et al, 2018), and Multiple Transform Selection (MTS).…”