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A robust adaptive video encoder based on human visual model

  • Letters
  • Published:
Journal of Electronics (China)

Abstract

A Robust Adaptive Video Encoder (RAVE) based on human visual model is proposed. The encoder combines the best features of Fine Granularity Scalable (FGS) coding, frame-dropping coding, video redundancy coding, and human visual model. According to packet loss and available bandwidth of the network, the encoder adjust the output bit rate by jointly adapting quantization step-size instructed by human visual model, rate shaping, and periodically inserting key frame. The proposed encoder is implemented based on MPEG-4 encoder and is compared with the case of a conventional FGS algorithm. It is shown that RAVE is a very efficient robust video encoder that provides improved visual quality for the receiver and consumes equal or less network resource. Results are confirmed by subjective tests and simulation tests.

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Author information

Authors and Affiliations

  1. Dept. of Electronics & Info. Eng., Huazhong University of Sci. & Tech., 430074, Wuhan

    Yin Hao, Zhang Jiangshan, Zhu Yaoting & Zhu Guangxi

Authors
  1. Yin Hao
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  2. Zhang Jiangshan
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  3. Zhu Yaoting
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  4. Zhu Guangxi
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Additional information

Supported by Innovation Fund of China(00C26224210641)

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Cite this article

Yin, H., Zhang, J., Zhu, Y. et al. A robust adaptive video encoder based on human visual model. J. of Electron.(China) 20, 142–149 (2003). https://doi.org/10.1007/s11767-003-0011-0

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  • DOI: https://doi.org/10.1007/s11767-003-0011-0

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