In Wyner-Ziv (WZ)Distributed Video Codec

in order to describe the change characteristics of the correlation noise residual sub-band more accurately

we propose a new correlation noise-modeling algorithm based on improved-FCM (Fuzzy C-Means)clustering. In our proposed method

for each decoded sub-band

the residual coefficient eigenvectors are formed with the residual coefficients from adjacent sub-bands

and then they are clustered into different categories by an improved fuzzy C-means clustering algorithm. In order to avoid the overflowing problem caused by having only a few samples in one category

a threshold method is adopted to estimate the correlation noise parameter

which is useful to help decode the corresponding sub-band. Then the reconstructed sub-band is used to update the next subband eigenvectors to obtain more accurate eigenvectors. All the sub-bands are decoded using the same process. Fuzzy C-means algorithm is sensitive to the initial clustering centers. A method that is to produce a random membership degree matrix before iteration can solve this problem to some extent. Considering both the algorithm performance and complexity

the subband residual coefficients are clustered to eight classes. The experimental results show that this method can simulate accurately the different channel noise characteristics of different region in one frame. What's more

the more complex the video motion

the more obvious the performance superiority of the new algorithm. Compared with that of the subband level Laplace method

the average online rate-distortion performance can be improved up to 1 dB. A new correlation noise model based on an improved fuzzy C-means clustering algorithm is proposed in this paper. The experiment results show that the rate-distortion performance based on the new algorithm is better than that of sub-band Laplacian solution and the Laplacian-Cauchy mixture model

and the more complex video motion

the more obvious performance gain for the new algorithm.