As one of the most adopted distributed video coding approaches in the literature, Wyner–Ziv (WZ) video coding is not yet on par with the motion-compensated predictive coding solutions with respect to rate–distortion (RD) performance. One of the essential reasons lies in the absence of reliable knowledge of the correlation statistics between source and side information. Most of the existing works assume a probability distribution of the statistical dependency to be Laplacian, which is not accurate but computationally cheap. In this paper, a correlation estimation based on Gaussian mixture model is proposed for the band-level correlation noise of discrete cosine transform domain Wyner–Ziv codec. The statistics of the correlation noise between WZ frame and corresponding side information is analyzed by considering the temporal correlation and quantization distortion. Accordingly, the model parameters for correlation noise are estimated offline and utilized online in consequent decoding. The simulation results of Kullback–Leibler divergence show that the proposed model has higher accuracy than the Laplacian one. Experimental results demonstrate that the WZ codec incorporated with the proposed model can achieve very competitive RD performance, especially for the sequence with high motion contents and large group of picture (GOP) size.