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Intra Prediction Method for Depth Video Coding by Block Clustering through Deep Learning.

Dong-Seok Lee1, Soon-Kak Kwon2

  • 1AI Grand ICT Research Center, Dong-eui University, Busan 47340, Republic of Korea.

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|December 23, 2022
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Summary
This summary is machine-generated.

This study introduces a novel neural network for depth video intra-picture prediction using block clustering. The method enhances prediction accuracy by effectively handling blocks with multiple clusters, improving depth video compression efficiency.

Keywords:
1D CNNclusteringdeep learningdepth video codingintra prediction

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Video Compression

Background:

  • Intra-picture prediction is crucial for video compression.
  • Depth video compression presents unique challenges due to spatial redundancies.
  • Existing methods struggle with blocks containing multiple distinct spatial features.

Purpose of the Study:

  • To develop an improved intra-picture prediction method for depth video.
  • To address the performance degradation in depth video intra-prediction for blocks with multiple clusters.
  • To enhance the efficiency and accuracy of depth video coding.

Main Methods:

  • A neural network-based block clustering approach for intra-picture prediction.
  • A spatial feature prediction network using 1D CNN and fully connected layers.
  • A clustering network with 4 CNN layers to identify pixel clusters.
  • Adaptive block scaling for network input and output.

Main Results:

  • Achieved up to 12.45% bit rate savings under identical distortion levels.
  • Demonstrated improved prediction performance for depth video blocks with multiple clusters.
  • Successfully applied neural networks for spatial feature extraction and clustering in video coding.

Conclusions:

  • The proposed neural network-based intra-prediction method effectively improves depth video compression.
  • Block clustering through neural networks offers a robust solution for complex spatial patterns.
  • The method shows significant potential for advancing video coding standards.