No-reference video quality measurement using neural networks

Authors:
Jihwan Choe;Kwon Lee;Chulhee Lee
Affiliations:
Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea;Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea;Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea
Venue:
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Year:
2009

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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.