A bitstream-layer model for video coding distortion assessment is proposed to realize real-time quality monitoring for H.264/AVC networked video. The model realizes real-time quality assessment for networked video without resorting to full decoding
but by simply analyzing the payload information such as the quantization parameters
bit-rate and motion vectors. Firstly
the relationship between the coding distortion and quantization parameters for different video sequences is modeled. Then
the quantization parameters and bit-rate are employed to predict the spatial complexities
and the motion vectors are employed to predict the temporal complexities. After empirical data analysis
a mathematical model for video quality assessment is established taking account of the spatial and temporal masking effect of the human visual system. Experimental results show the advanced performance of the proposed model. A comparison with the NR QANV-PA model shows that the proposed model gets an increment about 0.016 0 in the Pearson Correlation Coefficient between the objective scores and mean opinion scores and a decrement about 0.079 7 in the root mean square error.
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