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Accelerating inter-frame prediction in Versatile Video Coding via deep learning-based mode selection
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AI
DOI：10.1016/j.jvcir.2025.104653.png)
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Abstract
En 中文
Compared to its predecessor HEVC, VVC utilizes the Quad-Tree plus Multitype Tree (QTMT) structure for partitioning Coding Units (CU) and integrates a wider range of inter-frame prediction modes within its inter-frame coding framework. The incorporation of these innovative techniques enables VVC to achieve a substantial bitrate reduction of approximately 40% compared to HEVC. However, this efficiency boost is accompanied by a more than tenfold increase in encoding time. To accelerate the inter-frame prediction mode selection process, a FPMSN (Fast Prediction Mode Selection Network)-based method focusing on encoding acceleration during the non-partitioning mode testing phase is proposed in this paper. First, the execution results of the affine mode are collected as neural network input features. Next, FPMSN is proposed to extract critical information from multi-dimensional data and output the probabilities for each mode. Finally, multiple trade-off strategies are implemented to early terminate low-probability mode candidates.
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