Gradient based image/video softcast with grouped-patch collaborative reconstruction
Inspired by the recent image quality assessment (IQA) studies which indicate that the image gradient data reflects the visual information more reliably than the image pixels, gradient based transmission scheme was recently proposed to pursue better perceptual quality for wireless visual communication. This paper develops an effective method to reconstruct high quality image from the received noisy gradient data. The proposed method utilizes both local correlation and non-local similarity within the image signal to regularize the reconstruction image. Principle component analysis (PCA) is employed to learn signal-adaptive two-dimensional (2D) transform basis, and 3D transform is performed on grouped similar patches to further decorrelate the coefficients. In this way, distortions can be effectively suppressed via adaptive collaborative shrinkage on the transform coefficients. Experimental results demonstrate that the proposed method improves the reconstruction performance remarkably compared with the existing schemes.