一种基于粗糙集分类的图像压缩方法

IMAGE COMPRESSION METHOD BASED ON ROUGH SET CLASSIFICATION

作者在文中提出了一种基于粗糙集分类的智能图像压缩方法 ,图像子块经过 DCT变换、特征属性提取后 ,再利用粗糙集将 DCT域图像子块分为平坦块和边缘块两类 ,并针对不同的子块类别分别应用不同的 SOFM神经网络进行矢量量化 ,最终实现对图像的有效压缩。实验结果表明 ,该方法压缩比高 ,信噪比高 ,信道误码率低 ,解码速度快 ,图像恢复效果好。

In this paper we show an intelligent method based on rough sets classification for image compression. Rough sets classification is used to select features from image blocks in DCT domain and classifies the blocks into two classes, plain and edge blocks. Two different SOFM networks are used to quantify them respectively. The results of test with this method show high compression ratio, high signal to noise ratio, low errors of coding, high decoding speed and fine resuming effect on subject.