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Content aware video quality prediction model for HEVC encoded bitstream

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Abstract

In this paper, a novel content based video quality prediction model for High Efficiency Video Coding (HEVC) encoded video stream is proposed, which takes into account the quantization parameter (QP) and the newly proposed content type classification (CTC) metric. The CTC metric is derived by combining different types of information extracted from the encoded video sequences: temporal and spatial complexity, the standard deviation of the bitrate and the value of quantized transform coefficients. This metric can establish a logarithmic relationship with the quality of the video sequence, which is evidenced by extensive experimental results. The experimental results demonstrate that the proposed prediction model can achieve better correlation between the actual PSNR and the predicted PSNR in the training and testing process, and outperforms the other existing prediction methods in terms of accuracy. Furthermore, subjective testing results also show a good consistency between the proposed prediction metric and the subjective rankings.

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Acknowledgements

This work was supported by Natural Science Foundation of China under Grant No. 61671283, 61301113, and Shanghai National Natural Science under Grant No.13ZR14165.

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Authors and Affiliations

  1. Key Laboratory of Advance Display and System Application, Ministry of Education, Shanghai University, Shanghai, China

    Yongfang Wang, Kanghua Zhu, Jian Wu & Yun Zhu

  2. School of Communication and Information Engineering, Shanghai University, Shanghai, China

    Yongfang Wang

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  1. Yongfang Wang
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  2. Kanghua Zhu
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  3. Jian Wu
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  4. Yun Zhu
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Correspondence to Kanghua Zhu.

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Wang, Y., Zhu, K., Wu, J. et al. Content aware video quality prediction model for HEVC encoded bitstream. Multimed Tools Appl 76, 19191–19209 (2017). https://doi.org/10.1007/s11042-017-4574-4

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