Machine Learning Based Efficient Qt-Mtt Partitioning for VVC Inter Coding

The Joint Video Experts Team (JVET) have standardized the Versatile Video Coding (VVC) in 2020 targeting efficient coding of the emerging video services and formats such as 8K and immersive video streaming applications. VVC standard enhances the coding efficiency by 40% at the cost of an encoder com...

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Bibliographic Details
Main Authors: Tissier, A., Hamidouche, W., Vanne, J., Menard, D.
Format: Conference Proceeding
Language:English
Subjects:
complexity reduction
Complexity theory
Convolutional Neural Network (CNN)
Decision Tree (DT)
Encoding
Image resolution
Limiting
Machine learning
Machine Learning (ML)
Neural networks
Streaming media
Versatile Video Coding (VVC)
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Summary:The Joint Video Experts Team (JVET) have standardized the Versatile Video Coding (VVC) in 2020 targeting efficient coding of the emerging video services and formats such as 8K and immersive video streaming applications. VVC standard enhances the coding efficiency by 40% at the cost of an encoder computational complexity increase estimated to 859%(x8) compared to the previous standard High Efficiency Video Coding (HEVC). This work aims at reducing the complexity of the VVC encoder under the Random Access (RA) configuration. The proposed method takes advantage of the inter prediction in order to predict the split probabilities through a convolutional neural network. Our solution reaches 31.8% of complexity reduction for a negligible bitrate increase of 1.11% outperforming state-of-the-art methods.
ISSN:2381-8549
DOI:10.1109/ICIP46576.2022.9898052