relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4659/
title: Fast QTMT for H.266/VVC Intra Prediction using Early-Terminated Hierarchical CNN model
creator: Hoang, Van Xiem
creator: Nguyen, Quang Sang
creator: Dinh, Bao Minh
creator: Do, Ngoc Minh
creator: Dinh, Trieu Duong
subject: Communications
description: Versatile Video Coding (VVC) has been standardization in July 2020. Compared to previous High Efficiency Video Coding (HEVC) standard, VVC saves up to 50% bitrate for equal perceptual video quality. To reach this efficiency, Joint Video Experts Team (JVET) has introduced a number of improvement techniques to VVC model. As a result, the complexity of VVC encoding also greatly increases. One of  the new techniques affects to the growing of complexity is the quad-tree nested multi-type tree (QTMT) including binary split and ternary splits, which lead to a block in VVC with various shapes in both square and rectangle. Based on the  aforementioned information we propose in this paper a new deep learning based fast QTMT method. We use a learned  convolutional neural network (CNN) model namely EarlyTerminated Hierarchical CNN to predict the coding unit map and then fed into the VVC encoder to early terminate the block partitioning process. Experimental results show that the proposed method can save 30.29% encoding time with a  negligible BD-Rate increase.
date: 2021-10
type: Conference or Workshop Item
type: NonPeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4659/1/ATC_2021_IEEEStamped.pdf
identifier:   Hoang, Van Xiem and Nguyen, Quang Sang and Dinh, Bao Minh and Do, Ngoc Minh and Dinh, Trieu Duong  (2021) Fast QTMT for H.266/VVC Intra Prediction using Early-Terminated Hierarchical CNN model.  In: International Conference on Advanced Technologies for Communications, HoChiMinh city, Vietnam.