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Fast decision mechanism for ternary tree partitioning in VVC intra coding.

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Area of Science:

  • Video compression technology
  • Digital image processing
  • Machine learning for video coding

Background:

  • Versatile Video Coding (VVC) relies on complex coding unit (CU) partitioning for efficiency.
  • Ternary tree (TT) partitioning in VVC intra coding is computationally intensive due to rate-distortion cost calculations.
  • Current methods for optimal CU partition selection are time-consuming, hindering real-time applications.

Purpose of the Study:

  • To develop a fast decision mechanism for VVC's ternary tree (TT) partitioning in intra coding.
  • To reduce the computational complexity associated with rate-distortion optimization (RDO) for CU partitioning.
  • To enhance overall video encoding efficiency without compromising visual quality.

Main Methods:

  • Investigated the correlation between image structural features and TT partition patterns.
  • Developed an efficient scheme for feature representation and extraction to minimize computational load.
  • Constructed comprehensive datasets for training a predictive model for optimal TT partition selection.
  • Integrated a predictive model into the VVC Test Model (VTM) for pre-RDO feature analysis.

Main Results:

  • The proposed mechanism effectively predicts optimal TT partition patterns, enabling bypass of complex RDO calculations.
  • Experimental results show significant time-saving improvements compared to existing lightweight neural network algorithms.
  • The method achieved a favorable trade-off between prediction accuracy and model complexity.
  • Coding quality was preserved while substantially accelerating the video coding process.

Conclusions:

  • The image feature-based decision mechanism offers a computationally efficient approach to VVC intra coding.
  • This method significantly accelerates video encoding by intelligently skipping unnecessary TT partitioning computations.
  • The approach demonstrates superior performance in time-saving metrics and Bjøntegaard Delta Bit Rate (BDBR) compared to prior lightweight methods.