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Efficient optimization of partitioning algorithm in enhanced compression model beyond versatile video coding using convolutional neural networks
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DOI：10.1117/1.jei.35.3.033017.png)
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Abstract
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The enhanced compression model (ECM), an advancement beyond the versatile video coding (VVC) standard, further improves coding efficiency but introduces additional complexity in encoding. Similar to VVC's quad-tree with nested multitype tree partitioning, ECM adopts a flexible and adaptive partition structure that enhances compression performance but at the expense of greater encoding time owing to the computationally intensive rate-distortion cost evaluations. To address this challenge, we propose an efficient intra-partitioning algorithm that leverages deep-learning (DL) techniques through a convolutional neural network (CNN). Specifically, the method implements two CNN models to predict horizontal and vertical binary tree partitions at a 32 & times; 32 coding unit, which are then combined into a unified fast partitioning decision process for the ECM. Experimental results demonstrate that, compared with the ECM-10.0 reference software, the proposed CNN-based approach achieves up to 45.20% encoding time savings, with only a minimal impact on compression performance.
Keywords:
video compression
enhanced compression model
quad-tree with nested multitype tree
convolution neural network
computational complexity