Motion Estimation for Video Coding Based on Subspace Pursuit

In this paper, a motion estimation algorithm is proposed based on subspace pursuit, which can be used in video coding to reduce the temporal redundancy. The main idea of our proposed algorithm is that the process of motion estimation will be imagined as sparse representation, that is, the predicted block is grouped as an observation vector and the corresponding reference blocks in previous reconstructed frame are used to construct a sparse dictionary, then the sparse coefficients are calculated by subspace pursuit algorithm. In order to reduce the transmission of sparse coefficients, the idea of template matching is adopted. Moreover, our proposed method can be combined with traditional motion estimation algorithm to further enhance the inter prediction accuracy. Simulation results show that our proposed method can outperform the decoder motion vector derivation method in term of the peak signal to noise ratio (PSNR).