Abstract:

Compressed video Super-Resolution（SR） technique estimates High-Resolution（HR） images from a sequence of Low-Resolution（LR） observations, it has been a great focus for video SR. Based on the theory of regularization and projection to a convex set, a novel SR algorithm is developed and analyzed using the quantization information from the compressed bitstream. The regularized cost function using the temporal and spatial prior information is proposed. The iterative gradient descent algorithm is utilized to reconstruct the HR image. The reconstructed HR image projects to a convex set in the DCT domain. Experimental results demonstrate that the proposed algorithm has an improvement in terms of both objective and subjective quality, and it is applicable for compressed images.