No-reference video quality measurement using neural networks

Jihwan Choe, Kwon Lee, Chulhee Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Objective video quality measurements emerge as an important issue as multimedia data is increasingly transmitted over the channels where bandwidth may not be guaranteed. Among various objective models for video quality measurement, no-reference models have the largest application areas. In this paper, we propose a no-reference video quality assessment method for H.264 using artificial neural networks. Various features are extracted from H.264 bit-stream data and these features are inputted to a neural network. The neural network is trained to predict subjective video quality scores obtained by a number of evaluators. Experimental results show promising results, though a larger database would be required to train neural networks to provide robust performance.

Original languageEnglish
Title of host publicationDSP 2009:16th International Conference on Digital Signal Processing, Proceedings
DOIs
Publication statusPublished - 2009 Nov 20
EventDSP 2009:16th International Conference on Digital Signal Processing - Santorini, Greece
Duration: 2009 Jul 52009 Jul 7

Publication series

NameDSP 2009: 16th International Conference on Digital Signal Processing, Proceedings

Other

OtherDSP 2009:16th International Conference on Digital Signal Processing
CountryGreece
CitySantorini
Period09/7/509/7/7

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Choe, J., Lee, K., & Lee, C. (2009). No-reference video quality measurement using neural networks. In DSP 2009:16th International Conference on Digital Signal Processing, Proceedings [5201054] (DSP 2009: 16th International Conference on Digital Signal Processing, Proceedings). https://doi.org/10.1109/ICDSP.2009.5201054