The one-time learning hierarchical CMAC and the memory limited CA-CMAC for image data compression

Two methods to compress transmitted image data are proposed in this paper. The first method is the one-time learning hierarchical CMAC method and the second is the memory limited CA-CMAC method for image data compression and reconstruction. The one-time learning hierarchical CMAC method is used when a coarse image needs to be sent to the receiver initially and then the image quality is gradually improved at the request of the receiver. But, when the transmitting channel data is limited, the memory limited CA-CMAC method can be used to decrease the bit rate per pixel. Both proposed methods, unlike conventional compression methods, use no filtering technique in either compression or reconstruction. CMAC networks use a few hypercubes to learn the characteristics of the original image, so image data can be compressed without suffering from blocking effect or boundary effect. One-time learning is good enough for compressing image data, and it has a high SNR after reconstruction.