Accurately weighting subbands in temporal wavelet transform

Wavelet-based scalable video coding is a promising technique for streaming video applications; especially the motion compensated temporal filtering (MCTF). It usually decomposes the input video frames into several subbands in temporal domain. A quantization error in each subband contributes to the overall distortion in the ultimate reconstructed video with different weights. These weighting factors play an important role in allocating bit budget to different subbands and influence the coding performance greatly. In theory, these weighting factors can be derived from the wavelet filter, but they are not match the experimental data very well. This paper first proposes a probability model to estimate the influence of different motion connections in MCTF. And the zero-coefficient percentage of each subband is presented to model the influence of accumulated rounding error. These two proposed models can make a good estimation on the subband weighting factors. The experimental results show that they have the consistent trend with actual data