Fractally configured neural networks
Neural Networks
IEEE Transactions on Computers
Image compression using wavelet transform and multiresolution decomposition
IEEE Transactions on Image Processing
Hierarchical image coding via cerebellar model arithmetic computers
IEEE Transactions on Image Processing
Optimal progressive lossless image coding using reduced pyramids with variable decimation ratios
IEEE Transactions on Image Processing
Fast algorithms for DCT-domain image downsampling and for inverse motion compensation
IEEE Transactions on Circuits and Systems for Video Technology
Learning convergence of CMAC technique
IEEE Transactions on Neural Networks
Neighborhood sequential and random training techniques for CMAC
IEEE Transactions on Neural Networks
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
Hi-index | 0.00 |
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, differential 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.