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

A. Tissier, W. Hamidouche, J. Vanne, D. Menard

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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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.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Image Processing (ICIP)
Number of pages5
ISBN (Electronic)978-1-6654-9620-9
Publication statusPublished - 2022
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Image Processing - Bordeaux, France
Duration: 16 Oct 202219 Oct 2022

Publication series

NameProceedings : International Conference on Image Processing
ISSN (Electronic)2381-8549


ConferenceIEEE International Conference on Image Processing


  • Image resolution
  • Limiting
  • Neural networks
  • Machine learning
  • Streaming media
  • Encoding
  • Complexity theory
  • Versatile Video Coding (VVC)
  • complexity reduction
  • Machine Learning (ML)
  • Convolutional Neural Network (CNN)
  • Decision Tree (DT)

Publication forum classification

  • Publication forum level 1


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