A fast wavelet transform with high compression ratio results in a serious block phenomenon in self-organization feature mapping (SOFM) algorithmand a poor image restoration quality. To address the above problem

RSOFM-C vector quantization algorithm is proposed

in which the neural network relay neurons are introduced. The use of relay neurons addresses the problem of uneven code words by introducing the concept of relay neurons. Euclidean distance discriminant inequality is given in neural network middle layer. Neurons that failed to satisfy the distortion measure are excluded

thus reducing repeated calculation and accelerating the learning speed.SOFM-C algorithm and fast wavelet transform are combined according to the difference signal coding principle in DPCM. The low frequency image signal is further compressed by using the RSOFM-C algorithm. In the simulation experiment

the proposed algorithm is compared with similar compression method. At 52% compression ratio

the peak signal-to-noise ratio of this method reached 39.28 dB

which is higher than that of other methods. The compression algorithm can eliminate the blocking phenomenon

and a high quality reconstructed image can be obtained while ensuring high compression ratio. Experiment shows that by introducing the fast wavelet compression method of interneuron

images can be compressed with high compression ratio

fidelity

and speed.