Video coding based on compressive sensing and curvelet transform

Different from the traditional signal sampling, compressive sensing can capture and represent compressible signal at a rate below the Nyquist rate, and it is possible to reconstruct signals accurately and sometimes even exactly from far fewer data than what is usually considered necessary via using an optimization process which has broad applications such as compressive imaging, signal coding, etc. Then a new video coding framework based on compressive sensing and curvelet transform is proposed in this paper. This new framework uses compressive sensing to the key frame of test sequence in curvelet transform domain, and then gains recovery frame via Regularized Orthogonal Matching Pursuit algorithm to achieve data compress. The experiments show that this framework has better performance and lower RMSE than traditional method, and the number of measurements and sparsity level are the key point.