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A Video Quality Assessment Method for VoIP Applications Based on User Experience

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Abstract

An objective video quality assessment method is proposed to evaluate the video quality in voice over internet protocol (VoIP) applications under network distortion. The fluency and the clarity of videos are two main parts of the factors that affect user experience, thus the method evaluates these two parts to assess the distortions of videos in VoIP applications caused by codec and packet loss. The clarity of the video is measured by calculating block artifacts and frame blurring. Video blocking artifacts are measured by splitting the picture into small blocks and calculating the difference of the pixels around each border while video blurring is measured by getting edge information through Sobel operator, and counting the gradient histogram. Then the video clarity can be measured by a weighted sum of block artifacts score and blurring score using linear regression. The scores are also normalized in order to eliminate the impact of different video contents. The video fluency is calculated by counting the wrong frame in the video. Finally, a weighted sum of video clarity score and video fluency score can represent the quality of the video. The experimental results show that the objective quality scores have a strong correlation with the subjective quality scores, and the algorithm concludes two parts of user experience other than just image quality, which is more comprehensive and it can be used in video quality assessment in VoIP applications.

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Acknowledgements

This work was supported by the 863 program of China (Grant No. 2014AA01A701) and Beijing Natural Science Foundation (Grant No. 4152047).

Author information

Correspondence to Jing Wang.

Additional information

This article is part of the Topical Collection on Image Quality Assessment for Sensing.

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Cite this article

Wang, Z., Wang, J., Wang, F. et al. A Video Quality Assessment Method for VoIP Applications Based on User Experience. Sens Imaging 18, 12 (2017). https://doi.org/10.1007/s11220-017-0161-z

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Keywords

  • VoIP
  • Video quality assessment
  • Video quality
  • Video clarity
  • Video fluency