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This study introduces datasets for H.264 video streaming using Dynamic Adaptive Streaming over HTTP (DASH). Datasets capture video features and quality metrics across various resolutions and compression levels for enhanced Quality of Experience.

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Area of Science:

  • Computer Science
  • Multimedia Engineering

Background:

  • H.264 encoded videos dominate internet traffic.
  • Dynamic Adaptive Streaming over HTTP (DASH) is a popular video streaming technology.
  • DASH optimizes video delivery for Quality of Experience (QoE) based on receiver limitations.

Purpose of the Study:

  • To create comprehensive datasets for analyzing H.264 video segments used in DASH.
  • To extract spatio-temporal and color features from video segments across multiple resolutions.
  • To collect quality metrics for video segments encoded at different compression levels and resolutions.

Main Methods:

  • Generated datasets from 4065 two-second video segments.
  • Extracted color, spatial, and temporal features from segments at resolutions: 240p, 360p, 480p, 720p, 1080p, 1440p, and 4K.
  • Recorded quality metrics for segments encoded with varying compression levels at different resolutions.

Main Results:

  • Two distinct datasets were created, detailing video segment characteristics.
  • Feature extraction covered a wide range of resolutions, essential for adaptive streaming analysis.
  • Quality metrics provide insights into the impact of compression and resolution on video quality.

Conclusions:

  • The developed datasets offer valuable resources for research in video streaming optimization.
  • Analysis of these datasets can lead to improved Quality of Experience in DASH streaming.
  • Understanding feature variations and quality metrics is crucial for efficient H.264 video delivery.