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Through extensive experimentation with a large set of video sequences, we show that modeling the statistical distribution of the transform coefficients in H.264-like video coders can be improved significantly in terms of accuracy by classifying the video source into multiple classes and modeling each class with a different statistical distribution. In this paper, we present a simple yet effective classification method and best practical models for each class and show that it is possible to improve the statistical modeling significantly without a significant complexity increase. We propose a two-class based approach in which one class is composed of very low detail blocks, and the other class is composed of high texture blocks and blocks with edges. Our two-class based statistical modeling reduces the approximation error up to 70% over the existing single-class modeling approaches for majority of the video sequences experimented. Furthermore, this approach also fits very well with the context of rate control with human visual system considerations, in which distortion in low detail regions of an image is more noticeable than in high detailed regions. In this work, we consider modeling the transform coefficients all lumped together.