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Fast QTBT Partition Algorithm for Intra Frame Coding through Convolutional Neural Network
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DOI：10.1109/ACCESS.2018.2872492.png)
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Abstract
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The latest Joint Video Exploration Team employs quad-tree plus binary-tree (QTBT) block partitioning structure, which can improve coding performance significantly than High Efficiency Video Coding with hugely increased encoding complexity. To address this issue, we propose a novel fast QTBT partition method through a convolutional neural network (CNN). Specifically, the proposed algorithm uses CNN to predict the QTBT partition depth range of 32 x 32 block directly according to the inherent texture richness of the image, rather than to judge split or not at each depth level. For training optimization, we introduce a misclassification penalty term combined with L2 HingeLoss function, which can further boost the classification accuracy. Experimental results demonstrate the effectiveness of our proposed method; our rate-distortion maintaining setting can achieve 42.33% complexity reduction with just 0.69% bitrate increase. Our low complexity setting can achieve 62.08% complexity reduction with 2.04% bitrate increase.
Keywords:
Fast intra coding
convolutional neural network
coding units
quad-tree plus binary-tree partition
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