This paper presents a backward context adaptive coder for motion-compensated difference frames based on a mixture density model. In this model, each pixel is assumed to be generated from a random source with probability distribution conditioned on the interframe context which consists of the local intensity context and the local displacement vector context. To estimate this conditional probability distribution, a backward adaptive nonparametric approach is chosen in our work due to its fast adaptivity to the input data characteristics. Once this probability distribution is found, each pixel can then be coded by using the corresponding entropy coder. As an application, we modified Telenor's TMN5 H.263 algorithm based on this context adaptive coding idea and implemented a hybrid interframe coder. Our simulation results show that the coding performance at 8kbps is comparable to that of H.263 without obvious blocking effects and our coder can also be implemented with lower complexity.