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Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
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Perceptual video quality assessment in H.264 video coding standard using objective modeling.

Ramasamy Karthikeyan1, Gopalakrishnan Sainarayanan2, Subramaniam Nachimuthu Deepa3

  • 1Teclever Solutions Pvt Ltd, Bangalore, India.

Springerplus
|May 3, 2014
PubMed
Summary

This study introduces a no-reference objective model for H.264 video quality assessment. The model accurately estimates perceptual video quality by analyzing artifacts like blockiness and blur.

Keywords:
AVCBlockinessBlurH.264JerkinessMean opinion score [MOS]Perceptual video quality

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Area of Science:

  • Digital video processing
  • Image and video quality assessment

Background:

  • Widespread digital video usage necessitates robust quality measurement.
  • Current methods often rely on reference videos or bitrate alone.

Purpose of the Study:

  • Develop a no-reference objective model for perceptual video quality assessment in H.264 encoding.
  • Quantify quality degradation caused by encoding artifacts.

Main Methods:

  • Calculated quality impairments introduced by H.264 encoding (Intra/Inter prediction).
  • Developed a model to compute perceptual video quality metrics based on blockiness, blur, and jerkiness.
  • Compared model accuracy against established full-reference objective methods.

Main Results:

  • The proposed model effectively estimates perceptual video quality without requiring the original video.
  • Objective quality metrics derived from subjective impairments show improved accuracy over bitrate-only models.
  • Accuracy validated against popular full-reference objective methods.

Conclusions:

  • The developed no-reference model provides an accurate and efficient method for H.264 video quality assessment.
  • This approach addresses the industry's growing demand for reliable video quality measurement.