Complexity reduction of versatile video coding standard: a deep learning approach

被引:2
|
作者
Zouidi, Naima [1 ]
Belghith, Fatma [1 ]
Kessentini, Amina [1 ,2 ]
Masmoudi, Nouri [1 ]
机构
[1] Univ Sfax, Natl Sch Engn, Lab Elect & Informat Technol, Sfax, Tunisia
[2] Univ Gabes, Higher Inst Comp & Multimedia Gabes, Gabes, Tunisia
关键词
versatile video coding; intraprediction; quadtree plus binary tree; deep learning; complexity reduction;
D O I
10.1117/1.JEI.30.2.023002
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The new video coding standard, known as versatile video coding (VVC) is projected to be concluded by the end of 2020. This standard is conducted mainly to address 8k videos and emerging applications such as 360 deg and high dynamic range. Intraprediction is a part of the prediction step in the video coding that exploits spatial redundancy. This module has been improved, compared to the high-efficiency video coding (HEVC), by increasing the set of angular intraprediction modes (IPM) from 33 to 65 to model directional textures more accurately. Moreover, a quadtree plus binary tree (QTBT) structure replaced the QT of the HEVC. These improvements targeting at enhancing the coding efficiency resulted in significant coding complexity, especially in terms of encoding time. This paper fits into this context. It evokes the optimizations of the intramode and coding unit size decisions using statistical methods of fast decision and deep learning. A fast intramode decision algorithm is proposed for the different binary depths of the QTBT structure. Thus, an optimization by deep learning for square blocks is also included. Results show that the combinations of these two approaches can significantly reduce the complexity of the VVC encoder. Under the all intra (AI) configuration, a reduction of about 61.04% of the intraencoding time is achieved while maintaining an acceptable rate distortion performance. (c) 2021 SPIE and IS&T [DOI: 10.1117/1.JEI.30.2.023002] Video traffic is continuing to grow at a huge rate. According to a Cisco study,1 video consumption will surpass 80% of global IP traffic by 2022. Unsurprisingly, the emerging applications, such as 360 deg and high dynamic range videos, have rapidly gained great attention from video consumers and further advanced the shareability of video content. The foreseeable future will also attest to the dominance of beyond ultrahigh definition qualities and high frame rate videos. Due to this rapid evolution, the need for higher coding efficiency than that of the current standard
引用
收藏
页数:22
相关论文
共 50 条
  • [1] Complexity and Coding Efficiency Assessment of the Versatile Video Coding Standard
    Siqueira, Icaro
    Correa, Guilherme
    Grellert, Mateus
    2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2021,
  • [2] Complexity reduction methods for Versatile Video Coding: A comparative review
    Filipe, Jose N.
    Tavora, Luis M. N.
    Faria, Sergio M. M.
    Navarro, Antonio
    Assuncao, Pedro A. A.
    DIGITAL SIGNAL PROCESSING, 2025, 160
  • [3] CNN-LSTM Learning Approach-Based Complexity Reduction for High-Efficiency Video Coding Standard
    Bouaafia, Soulef
    Khemiri, Randa
    Maraoui, Amna
    Sayadi, Fatma Elzahra
    SCIENTIFIC PROGRAMMING, 2021, 2021
  • [4] A Method for Rate-Distortion-Complexity Optimization in Versatile Video Coding Standard
    Rezaeieh, Amir
    Roodaki, Hoda
    2021 26TH INTERNATIONAL COMPUTER CONFERENCE, COMPUTER SOCIETY OF IRAN (CSICC), 2021,
  • [5] Low-Complexity Error Resilient HEVC Video Coding: A Deep Learning Approach
    Wang, Taiyu
    Li, Fan
    Qiao, Xiaoya
    Cosman, Pamela C.
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 30 : 1245 - 1260
  • [6] Versatile video coding: A Next-generation Video Coding Standard
    Takamura, Seishi
    NTT Technical Review, 2019, 17 (06): : 49 - 52
  • [7] Low-Complexity Intra Coding in Versatile Video Coding
    Choi, Kiho
    The Van Le
    Choi, Yongho
    Lee, Jin Young
    IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2022, 68 (02) : 119 - 126
  • [8] Selective Encryption of the Versatile Video Coding Standard
    Farajallah, Mousa
    Gautier, Guillaume
    Hamidouche, Wassim
    Deforges, Olivier
    El Assad, Safwan
    IEEE ACCESS, 2022, 10 : 21821 - 21835
  • [9] Deep learning-based video quality enhancement for the new versatile video coding
    Soulef Bouaafia
    Randa Khemiri
    Seifeddine Messaoud
    Olfa Ben Ahmed
    Fatma Ezahra Sayadi
    Neural Computing and Applications, 2022, 34 : 14135 - 14149
  • [10] Deep learning-based video quality enhancement for the new versatile video coding
    Bouaafia, Soulef
    Khemiri, Randa
    Messaoud, Seifeddine
    Ben Ahmed, Olfa
    Sayadi, Fatma Ezahra
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (17): : 14135 - 14149