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Early quadtree with nested multitype tree partitioning algorithm based on convolution neural network for the versatile video coding standard

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JOURNAL OF ELECTRONIC IMAGING 2024年第4期33卷

作者： Abdallah, Bouthaina Ben Jdidia, Sonda Belghith, Fatma Ben Ayed, Mohamed Ali Masmoudi, Nouri Univ Sfax Natl Engn Sch Sfax Elect & Informat Technol Lab Sfax Tunisia Univ Sfax Sfax Natl Sch Elect & Commun New Technol & Telecom Syst Lab Sfax Tunisia

The Joint Video Experts Team has recently finalized the versatile video coding (VVC) standard, which incorporates various advanced encoding tools. These tools ensure great enhancements in the coding efficiency, leading to a bitrate reduction up to 50% when compared to the previous standard, high-efficiency video coding. However, this enhancement comes at the expense of high computational complexity. Within this context, we address the new quadtree (QT) with nested multitype tree partition block in VVC for all-intra configuration. In fact, we propose a fast intra-coding unit (CU) partition algorithm using various convolution neural network (CNN) classifiers to directly predict the partition mode, skip unnecessary split modes, and early exit the partitioning process. The proposed approach first predicts the QT depth at a CU of size 64 x 64 by the corresponding CNN classifier. Then four CNN classifiers are applied to predict the partition decision tree at a CU of size 32 x 32 using multithreshold values and ignore the rate-distortion optimization process to speed up the partition coding time. Thus the developed method is implemented on the reference software VTM 16.2 and tested for different video sequences. The experimental results confirm that the proposed solution achieves an encoding time reduction of about 46% in average, reaching up to 67.3% with an acceptable increase in bitrate and an unsignificant decrease in quality. (c) 2024 SPIE and IS&T

关键词： versatile video coding computational complexity quadtree with nested multitype tree convolution neural network encoding time reduction

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Fast mode decision for CU coding based on CNN for the VVC standard

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JOURNAL OF ELECTRONIC IMAGING 2022年第5期31卷 053004-053004页

作者： Abdallah, Bouthaina Belghith, Fatma Ben Ayed, Mohamed Ali Masmoudi, Nouri Univ Sfax Natl Engn Sch Sfax Elect & Informat Technol Lab Sfax Tunisia Univ Sfax Natl Sch Elect & Commun ENETCOM New Technol & Telecom Syst Lab NTSCOM Sfax Tunisia

Versatile Video Coding (VVC) is the latest generation of the video coding standard. In VVC, the advanced quadtree with a nested multitype tree (QTMT) partition structure provides more flexible coding unit (CU) partition sizes compared with the quadtree (QT) decision tree structure applied in the previous High Efficiency Video Coding (HEVC) standard. This flexibility, achieved by the new QTMT partitioning improvement, considerably improves the coding performance while increasing the coding computational complexity caused mainly by the rate distortion optimization processing. To overcome the complexity issue, a fast deep intra QTMT decision tree approach based on a convolution neural network (CNN) is adopted to determine the QTMT depth decision of each 128 x 128 Coding tree Unit (CTU). The proposed algorithm predicts both the BT depths at 32 x 32 CUs and the QT depths at 64 x 64 using trained CNNs designed for each structure instead of processing the RDcost. Experimental results prove that the suggested deep QTMT approach achieves an important complexity reduction of up to 55.51% compared with the original reference software VTM3.0, with an average of about 35% encoding time reduction accompanied by an insignificant loss in encoding performance. (c) 2022 SPIE and IS&T

关键词： Versatile Video Coding quadtree with nested multitype tree computational complexity convolution neural network complexity reduction

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