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Advanced ANN Architecture for the CTU Partitioning in All Intra HEVC.

Jakub Kwaśniak1, Mateusz Majtka1, Mateusz Lorkiewicz1

  • 1Institute of Multimedia Telecommunications, Poznan University of Technology, pl. M. Skłodowskiej-Curie 5, 60-965 Poznań, Poland.

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Summary
This summary is machine-generated.

This study accelerates video encoding using artificial neural networks (ANNs). ResNet and DenseNet architectures significantly reduce encoding time for HEVC CTU partitioning, balancing efficiency and speed.

Keywords:
DenseNetHEVCResNetartificial neural networksvideo encoding

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Video Compression

Background:

  • Video encoder complexity necessitates parameter optimization.
  • Neural networks are increasingly applied to accelerate video encoding processes.

Purpose of the Study:

  • To evaluate ResNet and DenseNet architectures for accelerating the CTU partitioning algorithm in HEVC (High Efficiency Video Coding).
  • To analyze the trade-offs between compression efficiency, network size, and encoding time reduction.

Main Methods:

  • Implementation and evaluation of various ResNet- and DenseNet-type neural network architectures.
  • Testing on HEVC All Intra mode for CTU partitioning.
  • Exhaustive performance analysis.

Main Results:

  • Demonstrated significant encoding time reduction using the proposed neural network architectures.
  • Evaluated compression efficiency and network size impacts.
  • Quantified performance improvements across different architectures.

Conclusions:

  • ResNet and DenseNet architectures offer viable solutions for accelerating HEVC CTU partitioning.
  • Architectural choices impact compression efficiency, network size, and speed.
  • Considerations for hardware-constrained environments and small devices are discussed.