MAXIMUM A POSTERIORI SUPER-RESOLUTION OF COMPRESSED VIDEO USING A NEW MULTICHANNEL IMAGE PRIOR

Super-resolution (SR) algorithms for compressed video aim at recovering high-frequency information and estimating a high-resolution (HR) image or a set of HR images from a sequence of low-resolution (LR) video frames. In this paper we present a novel SR algorithm for compressed video based on the maximum a posteriori (MAP) framework. We utilize a new multichannel image prior model, along with the state-of-the art image prior and observation models. Moreover, relationship between model parameters and the decoded bit-stream are established. Numerical experiments demonstrate the improved performance of the proposed method compared to existing algorithms for different compression ratios.