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Communication Dans Un Congrès Année : 2022

Light-weight cnn-based vvc inter partitioning acceleration

Résumé

The Versatile Video Coding (VVC) standard has been finalized by Joint Video Exploration Team (JVET) in 2020. Compared to the High Efficiency Video Coding (HEVC) standard, VVC offers about 50% compression efficiency gain, in terms of Bjontegaard Delta-Rate (BD-rate), at the cost of about 10x more encoder complexity [1]. In this paper, we propose a Convolutional Neural Network (CNN)-based method to speed up inter partitioning in VVC. Our method operates at the Coding Tree Unit (CTU) level, by splitting each CTU into a fixed grid of 8×8 blocks. Then each cell in this grid is associated with information about the partitioning depth within that area. A lightweight network for predicting this grid is employed during the rate-distortion optimization to limit the Quaternary Tree (QT)-split search and avoid partitions that are unlikely to be selected. Experiments show that the proposed method can achieve acceleration ranging from 17% to 30% in the Ran-domAccess Group Of Picture 32 (RAGOP32) mode of VVC Test Model (VTM)10 with a reasonable efficiency drop ranging from 0.37% to 1.18% in terms of BD-rate increase.
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Dates et versions

hal-03808305 , version 1 (10-10-2022)

Identifiants

  • HAL Id : hal-03808305 , version 1

Citer

Yiqun Liu, Mohsen Abdoli, Thomas Guionnet, Christine Guillemot, Aline Roumy. Light-weight cnn-based vvc inter partitioning acceleration. IVMSP 2022 - IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop, Jun 2022, Nafplio, Greece. pp.1-5. ⟨hal-03808305⟩
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