Linear model-based adaptive prediction for video decoding complexity

This paper proposes a novel approach to predict the video decoding computational complexity. The decoding complexity of each frame is found having an approximate linear relationship with the frame length, whereas the motion Information (MI) and the amount of encoded residual coefficients (ERC) are proved to be the two main factors that affect the variation of the model parameters. The changing rule of MI and ERC for different kinds of video contents are investigated, which in turn derives the variation regularity of the model parameters. Then the correlation between the model parameters of neighboring frames is defined as a piecewise function under the constraint of video motion complexity. The derived piecewise function is used to predict the decoding complexity online, and the prediction error is utilized as the feedback to update the model parameters adaptively. Experimental results show that the proposed method can give fairly accurate prediction for the decoding complexity with very low overhead. The average prediction errors for various test sequences are all within 7%.