Gradient based image transmission and reconstruction using non-local gradient sparsity regularization

Most existing image coding and communication systems aim to minimize the mean square error (MSE) of the pixels reconstructed at receivers. However, the quality metric MSE has long been criticized for not being consistent with the perception of human vision systems. This paper considers a gradient-based image SoftCast (G-Cast) scheme, based on the recent advancements in image quality assessment which indicate that gradient similarity is highly correlated with perceptual image quality. To reconstruct the image from the received noisy gradient data, we exploit the statistical characteristics of image gradients. Instead of using the very simple Laplacian distribution for image gradient as in the total variation (TV) model, we further exploit the non-local similarity of image patches. A non-local gradient sparsity regularization (NLGSR) method is developed and solved using augmented Lagrangian method. Experimental results show that the proposed scheme provides promising perceptual image quality, and the NLGSR reconstruction scheme outperforms the existing schemes remarkably.