Blind Natural Video Quality Prediction via Statistical Temporal Features and Deep Spatial Features

Jari Korhonen, Yicheng Su, Junyong You

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

43 Citations (Scopus)

Abstract

Due to the wide range of different natural temporal and spatial distortions appearing in user generated video content, blind assessment of natural video quality is a challenging research problem. In this study, we combine the hand-crafted statistical temporal features used in a state-of-the-art video quality model and spatial features obtained from convolutional neural network trained for image quality assessment via transfer learning. Experimental results on two recently published natural video quality databases show that the proposed model can predict subjective video quality more accurately than the publicly available video quality models representing the state-of-the-art. The proposed model is also competitive in terms of computational complexity.

Original languageEnglish
Title of host publicationMM 2020 - Proceedings of the 28th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages3311-3319
Number of pages9
ISBN (Electronic)9781450379885
DOIs
Publication statusPublished - 12 Oct 2020
Event28th ACM International Conference on Multimedia, MM 2020 - Virtual, Online, United States
Duration: 12 Oct 202016 Oct 2020

Conference

Conference28th ACM International Conference on Multimedia, MM 2020
Country/TerritoryUnited States
CityVirtual, Online
Period12/10/2016/10/20

Bibliographical note

Funding Information:
This work was supported in part by Natural Science Foundation of China under grant 61772348.

Publisher Copyright:
© 2020 ACM.

Keywords

  • convolutional neural network
  • human visual system
  • machine learning
  • video quality assessment

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