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Fast Intra-Prediction Mode Decision Algorithm for Versatile Video Coding Based on Gradient and Convolutional Neural Network
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DOI：10.3390/electronics14102031.png)
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Abstract
En 中文
The latest Versatile Video Coding(H.266/VVC) standard introduces the QTMT structure, enabling more flexible block partitioning and significantly enhancing coding efficiency compared to its predecessor, High-Efficiency Video Coding (H.265/HEVC). However, this new structure results in changes to the size of Coding Units (CUs). To accommodate this, VVC increases the number of intra-prediction modes from 35 to 67, leading to a substantial rise in computational demands. This study presents a fast intra-prediction mode selection algorithm that combines gradient analysis and CNN. First, the Laplace operator is employed to estimate the texture direction of the current CU block, identifying the most probable prediction direction and skipping over half of the redundant candidate modes, thereby significantly reducing the number of mode searches. Second, to further minimize computational complexity, two efficient neural network models, MIP-NET and ISP-NET, are developed to determine whether to terminate the prediction process for Matrix Intra Prediction(MIP) and Intra Sub-Partitioning(ISP) modes early, avoiding unnecessary calculations. This approach maintains coding performance while significantly lowering the time complexity of intra-prediction mode selection. Experimental results demonstrate that the algorithm achieves a 35.04% reduction in encoding time with only a 0.69% increase in BD-BR, striking a balance between video quality and coding efficiency.
Keywords:
versatile video coding (VVC)
mode prediction
convolutional neural network (CNN)
gradient
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