Super-Resolution Reconstruction of Compressed Video Based on Adaptive Quantization Constraint Set

Super-resolution (SR) technique is to estimate the high-resolution (HR) images by combining the non-redundant information that is available into a set of low-resolution (LR) images, which has been a great focus for compressed video. Based on the theory of projection onto convex set (POCS), this paper constructs quantization constraint set (QCS) using the motion between the frames and the quantization information embedded from the video bit stream. By combing the statistical properties of image and the human visual system (HVS), a novel adaptive quantization constraint set (AQCS) is proposed. The proposed algorithm and its performance analysis are also described. Simulation results show that AQCS-based SR algorithm obtains better performance in both objective and subjective quality, which is applicable for compressed video