引入神经网络中间神经元的快速小波图像压缩

目的针对自组织特征映射(SOFM)算法会出现严重的分块现象和快速小波变换在高压缩比的情况下图像恢复质量差的问题,提出引入神经网络中间神经元(relay neurons)的RSOFM-C矢量量化算法。方法引入了中间神经元的概念,使用中间神经元有效解决了码字利用不均匀的问题,并在神经网络中间层给出了欧氏距离不等式判据,排除不满足失真测度的神经元,减少重复计算,加快学习速度。根据差分脉冲编码调制(DPCM)中的差值信号编码原理将RSOFM-C算法与快速小波变换结合,使用RSOFM-C算法对由快速小波变换得到的图像低频信号进一步压缩。结果在仿真实验中,将本文算法与同类压缩方法进行对比,当压缩比为52%时,本文算法的峰值信噪比(PSNR)达到了39.28 d B,远远高于其他方法。结果表明,本文的压缩算法消除了分块现象,并且在保证高压缩比的同时获得高质量的重构图像。结论实验结果表明,本文提出的引入了中间神经元的快速小波压缩方法,具有高压缩比、高保真、速度快等优点,可以高效地压缩图像。

Fast wavelet image compression introducing neural network of relay neuron

Objective 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. Method 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. Result 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. Conclusion Experiment shows that by introducing the fast wavelet compression method of interneuron, images can be compressed with high compression ratio, fidelity, and speed.