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A ROBUST ADAPTIVE VIDEO ENCODER BASED ON HUMAN VISUAL MODEL 被引量:2

A ROBUST ADAPTIVE VIDEO ENCODER BASED ON HUMAN VISUAL MODEL
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摘要 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, framedropping 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. 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.
出处 《Journal of Electronics(China)》 2003年第2期142-149,共8页 电子科学学刊(英文版)
基金 Supported by Innovation Fund of China(00C26224210641)
关键词 FGS coding Redundancy coding Human visual model Rate shaping MPEG 4 视觉模型 MPEG-4 图像比例 冗余编码
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参考文献5

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同被引文献13

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