Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding

In mid-2020, the new international video coding standard, namely versatile video coding (VVC), was officially released by the Joint Video Expert Team (JVET). As its name indicates, the VVC enables a higher level of versatility with better compression performance compared to its predecessor, high-eff...

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Main Authors: Naima Zouidi, Amina Kessentini, Wassim Hamidouche, Nouri Masmoudi, Daniel Menard
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/11/23/4001
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author Naima Zouidi
Amina Kessentini
Wassim Hamidouche
Nouri Masmoudi
Daniel Menard
author_facet Naima Zouidi
Amina Kessentini
Wassim Hamidouche
Nouri Masmoudi
Daniel Menard
author_sort Naima Zouidi
collection DOAJ
description In mid-2020, the new international video coding standard, namely versatile video coding (VVC), was officially released by the Joint Video Expert Team (JVET). As its name indicates, the VVC enables a higher level of versatility with better compression performance compared to its predecessor, high-efficiency video coding (HEVC). VVC introduces several new coding tools like multiple reference lines (MRL) and matrix-weighted intra-prediction (MIP), along with several improvements on the block-based hybrid video coding scheme such as quatree with nested multi-type tree (QTMT) and finer-granularity intra-prediction modes (IPMs). Because finding the best encoding decisions is usually preceded by optimizing the rate distortion (RD) cost, introducing new coding tools or enhancing existing ones requires additional computations. In fact, the VVC is 31 times more complex than the HEVC. Therefore, this paper aims to reduce the computational complexity of the VVC. It establishes a large database for intra-prediction and proposes a multitask learning (MTL)-based intra-mode decision framework. Experimental results show that our proposal enables up to 30% of complexity reduction while slightly increasing the Bjontegaard bit rate (BD-BR).
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spelling doaj.art-c10a6be8248547c6824a01e2eef1899d2023-11-24T10:49:03ZengMDPI AGElectronics2079-92922022-12-011123400110.3390/electronics11234001Multitask Learning Based Intra-Mode Decision Framework for Versatile Video CodingNaima Zouidi0Amina Kessentini1Wassim Hamidouche2Nouri Masmoudi3Daniel Menard4The Institute of Electronics and Digital Technologies, UMR CNRS 6164, Electronics and Industrial Computing Department, The National Institute of Applied Sciences, University of Rennes, 35000 Rennes, FranceLaboratory of Electronics and Information Technologies, Electronic Department, National School of Engineers, University of Sfax, Sfax 3038, TunisiaThe Institute of Electronics and Digital Technologies, UMR CNRS 6164, Electronics and Industrial Computing Department, The National Institute of Applied Sciences, University of Rennes, 35000 Rennes, FranceLaboratory of Electronics and Information Technologies, Electronic Department, National School of Engineers, University of Sfax, Sfax 3038, TunisiaThe Institute of Electronics and Digital Technologies, UMR CNRS 6164, Electronics and Industrial Computing Department, The National Institute of Applied Sciences, University of Rennes, 35000 Rennes, FranceIn mid-2020, the new international video coding standard, namely versatile video coding (VVC), was officially released by the Joint Video Expert Team (JVET). As its name indicates, the VVC enables a higher level of versatility with better compression performance compared to its predecessor, high-efficiency video coding (HEVC). VVC introduces several new coding tools like multiple reference lines (MRL) and matrix-weighted intra-prediction (MIP), along with several improvements on the block-based hybrid video coding scheme such as quatree with nested multi-type tree (QTMT) and finer-granularity intra-prediction modes (IPMs). Because finding the best encoding decisions is usually preceded by optimizing the rate distortion (RD) cost, introducing new coding tools or enhancing existing ones requires additional computations. In fact, the VVC is 31 times more complex than the HEVC. Therefore, this paper aims to reduce the computational complexity of the VVC. It establishes a large database for intra-prediction and proposes a multitask learning (MTL)-based intra-mode decision framework. Experimental results show that our proposal enables up to 30% of complexity reduction while slightly increasing the Bjontegaard bit rate (BD-BR).https://www.mdpi.com/2079-9292/11/23/4001versatile video codingintra-predictionrate distortionfast intra-prediction decisionmultitask learning
spellingShingle Naima Zouidi
Amina Kessentini
Wassim Hamidouche
Nouri Masmoudi
Daniel Menard
Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding
Electronics
versatile video coding
intra-prediction
rate distortion
fast intra-prediction decision
multitask learning
title Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding
title_full Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding
title_fullStr Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding
title_full_unstemmed Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding
title_short Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding
title_sort multitask learning based intra mode decision framework for versatile video coding
topic versatile video coding
intra-prediction
rate distortion
fast intra-prediction decision
multitask learning
url https://www.mdpi.com/2079-9292/11/23/4001
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