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Texture-based block partitioning method for motion compensated frame interpolation.

Ho Sun Jung1, Myung Hoon Sunwoo2

  • 1Korea Testing Laboratory, 10, Chungui-ro, Jinju-si, Gyeongsangnam-do Korea.

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|November 8, 2016
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Summary

This study introduces a new motion compensated frame interpolation (MCFI) method using texture-based wedgelet partitioning (TWP) and multiple prediction based search (MPS). The novel MCFI algorithm enhances video quality and reduces computational load.

Keywords:
Block matching algorithm (BMA)Distributed video coding (DVC)Frame interpolationFrame rate up-conversion (FRUC)Motion estimation (ME)

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Area of Science:

  • Computer Vision
  • Image Processing
  • Video Enhancement

Background:

  • Frame interpolation is crucial for smooth video playback.
  • Existing motion compensated frame interpolation (MCFI) algorithms struggle with detailed object boundaries.
  • Need for improved accuracy and efficiency in video frame generation.

Purpose of the Study:

  • To develop a novel MCFI algorithm for enhanced video quality.
  • To improve the representation of detailed motions, especially around object boundaries.
  • To reduce computational complexity compared to existing methods.

Main Methods:

  • Texture-based wedgelet partitioning (TWP) for accurate object region approximation.
  • Multiple prediction based search (MPS) for reliable motion vector estimation.
  • Integration of TWP and MPS for a robust MCFI framework.

Main Results:

  • Achieved up to 2.93 dB improvement in average peak signal-to-noise ratio (PSNR).
  • Demonstrated superiority by up to 0.0256 in average structural similarity (SSIM).
  • Reduced computational complexity by up to 66.9% compared to existing MCFI algorithms.

Conclusions:

  • The proposed MCFI algorithm significantly enhances video interpolation performance.
  • TWP and MPS effectively address limitations of current MCFI techniques.
  • Offers a computationally efficient solution for high-quality video frame interpolation.