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Related Experiment Video

Updated: Aug 29, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Object-Cooperated Ternary Tree Partitioning Decision Method for Versatile Video Coding.

Sujin Lee1, Sang-Hyo Park1, Dongsan Jun2

  • 1School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an object-cooperated method to reduce encoding complexity in Versatile Video Coding (VVC) by efficiently partitioning ternary trees (TT). Object detection features improve decision-making for TT partitioning, outperforming existing machine learning approaches.

Keywords:
encoding complexitymachine learningobject detectionternary treeversatile video coding

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Versatile Video Coding (VVC) faces challenges in encoding complexity.
  • Existing methods for reducing VVC complexity often rely on encoding context, neglecting object-level information.
  • Ternary tree (TT) partitioning is a key area for complexity reduction in VVC.

Purpose of the Study:

  • To propose an object-cooperated decision method for efficient ternary tree (TT) partitioning in VVC.
  • To reduce the encoding complexity of Versatile Video Coding (VVC) by leveraging object detection.
  • To develop machine learning-based classifiers for optimizing TT-split decisions.

Main Methods:

  • Applied an object detection model to extract high-level object features (number, ratio) from video frames.
  • Developed machine learning (ML)-based binary classifiers for TT-split direction decisions (vertical/horizontal).
  • Formulated the TT-split decision as a binary classification problem using extracted object features.

Main Results:

  • The proposed object-cooperated method effectively reduces VVC encoding complexity.
  • The ML-based classifiers accurately decide whether TT-split processes can be skipped.
  • Experimental results demonstrate superior performance compared to a state-of-the-art ML-based model.

Conclusions:

  • Object-level features are crucial for efficient VVC encoding complexity reduction.
  • The proposed method offers a novel and effective approach to optimize TT partitioning in VVC.
  • Leveraging object detection in conjunction with ML classifiers significantly enhances VVC encoding efficiency.