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A Robust Adaptive Video Encoder (RAVE) based on human visual model is pro-posed. 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 lossand available bandwidth of the network, the encoder adjust the output bit rate by jointly adaptingquantization step-size instructed by human visua1 model, rate shaping, and periodically inserting
A Robust Adaptive Video Encoder (RAVE) based on human visual model is pro-posed. 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 lossand available bandwidth of the network, the encoder adjust the output bit rate by jointly adaptingquantization step-size instructed by human visua1 model, rate shaping, and inserting