Closed-loop method to improve image PSNR in pyramidal CMAC networks

A closed-loop method to improve image the peak signal to noise ratio (PSNR) in pyramidal cerebellar model arithmetic computer (CMAC) networks is proposed in this paper. We propose a novel coding procedure, which can make the CMAC network learn the feature of the transmitted image with only one-shot training, so some sampled data of the original image can quickly be sent to reconstruct a coarse image. In the meantime, di€erential codes are transmitted to improve the image quality using the closed-loop method in pyramidal CMAC networks. As a result, the quality of the reconstructed image can be improved at the bottom of the pyramidal CMAC networks. Finally, the experimental results demonstrate that the proposed method can give higher PSNR at a lower bit rate after reconstruction, when it is applied to JPEG compression.