推广的多值指数双向联想记忆模型及其应用

An Extended Multi-Valued Exponential Bi-Directional Associative Memory Model and Its Application

推广了Wang的多值指数双向联想记忆(multi-valued exponential bi-directional associative memory,简称MV-eBAM)模型,使其成为所提出的推广的多值指数双向联想记忆 (extended MV-eBAM,简称EMV-eBAM) 模型的一个特例.EMV-eBAM具有比前者更高的存储容量和纠错性能,因此利用这种性能,设计了一种基于联想记忆的新型图像压缩算法.该算法在无噪声情况下具有与矢量量化(vector quantization,简称VQ)算法相近的性能,而在双重(信道和图像)噪声环境下则具有显著的抑制效果.对比实验结果显示,在添加5%椒盐噪声下,该算法几乎能完全排除噪声干扰,而VQ则反而放大了噪声.该算法的另一个优点是,当在差错信道中传送时,可以获得比采用循环纠错码更强的纠错性能.因而,该算法具有较强的鲁棒性.

An extended multi-valued exponential bi-directional associative memory (EMV-eBAM) model is presented in this paper based on Wang抯 MV-eBAM model, which is a special case of EMV-eBAM (extended MV-eBAM). EMV-eBAM has higher storage capacity and stronger error-correcting capability. Using these performances in image compression, a novel image compression algorithm based on EMV-eBAM is proposed. In noise-free situations, this algorithm can acquire similar performances compared with vector quantization algorithm (VQ). However, in noisy context, this algorithm possesses strong noise-restraining capability. The experimental results show that while VQ amplified 5% random noises appended in the image, this algorithm can hold back nearly all noises and acquire similar performances as in noise-free context. Furthermore, in transmitting there may be some errors in the channel, in this situation, this algorithm has much better error-correcting capability than the result by using the cyclic encoding method, so this algorithm is a robust image compression algorithm.