Design of neuro-fuzzy network for image compression

The main objective of this paper is to propose a Neuro-Fuzzy based algorithm for Image compression. The inputs to the network are original image data, while the outputs are reconstructed image data, which are close to the inputs. If the amount of data required to store the hidden unit values and the connection weights to the output layer is less than the original data, compression is achieved. The compression ratio achieved in this paper is about 9 with good reconstructed image quality. The proposed network has an additional feature that each addition of a hidden unit to the network will always improve the image quality. Further the user can trade between image quality and compression ratio depending on the application requirement. The results are found to be better than the conventional methods.