基于量子BP网络的图像压缩技术研究

量子神经网络是一门崭新的学科,是量子理论和人工神经网络结合的产物。它融合了量子计算与神经网络的优点,具有很高的理论价值和应用潜力。本文基于具有量子输入和量子输出的量子神经元模型,利用BP网络用于图像压缩的原理,同时借助复数BP算法提出了QBP算法,构建一种用于图像压缩的3层QBP网络模型,实现了图像压缩与图像重建。仿真结果表明,在与BP网络压缩比相同的情况下,QBP网络不仅获取较好的重建图像质量,而且在最佳学习速率下迭代次数比BP网络少。

Research of image compression based on quantum BP network

Quantum Neural Network （QNN）, which integrates the characteristics of Artificial Neural Network （ANN） with quantum theory, is a new study field. It takes advantages of ANN and quantum computing and has a high theoretical value and potential applications. Based on quantum neuron model with a quantum input and output quantum and artificial neural network theory, at the same time, QBP algorithm is proposed on the basis of the complex BP algorithm, the network of a 3-layer quantum BP which implements image compression and image reconstruction is built. The simulation results show that QBP can obtain the reconstructed images with better quantity compared with BP in spite of the less learning iterations.