Fast Wavelet Image Compression Research Based On Interneuron Neural Network

There would be a serious block phenomenon in SOFM algorithm, and a poor image restoration quality when fast wavelet transform is in the case of the high compression ratio. To solve the above problem, RSOFM-C vector quantization algorithm is proposed, in which neural network relay neurons are introduced. The use of relay neurons solves 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 do not satisfy the distortion measure are excluded, it reduces the repeated calculation and accelerate the learning speed. According to the difference signal coding principle in DPCM, the SOFM-C algorithm and the fast wavelet transform are combined. The low frequency image signal is further compressed by using the RSOFM-C algorithm. In the simulation experiment, the algorithm in this paper is compared with similar compression method and the peak signal to noise ratio of this method (PSNR) is much higher than other methods. The compression algorithm in this paper eliminated the blocking phenomenon, and high quality reconstructed image can be obtained while ensuring the high compression ratio. Experiment showed that by introducing the fast wavelet compression method of interneuron, there are advantages of high compression ratio, fidelity and speed, images can be compressed efficiently.