Abstract: Binary Empirical Mode Decomposition（BEMD） is a kind of adaptive method of data-driven，which is primary function dependent.Principle Component Analysis algorithm has an excellent performance in decorrelation and high compression ratio.Therefore，in this paper，a new method for compressing is proposed，which is implemented by first decomposing the original image through BEMD and then using the PCA algorithm to compress the decomposed subimages.By simulating on the Matlab，the validity and the superiority of this method are proved，and basically achieve the goal of a high signal to noise ratio under a high compression ratio.

Key words: Binary Empirical Mode Decomposition（BEMD）, Principle Component Analysis（PCA）, Peak Signal to Noice Ratio（PSNR）, digital image compression

摘要： 二维经验模式分解（BEMD）方法是一种不依懒于基函数的数据驱动的自适应方法，主成分分析（PCA）算法具有去相关性好、压缩比高等特点。因此尝试运用BEMD算法对图像进行分解，利用PCA算法对分解后的子图像进行压缩。通过Matlab仿真，证明了该方法的有效性和优越性，且基本实现了高压缩比下达到高信噪比的目的。

关键词: 二维经验模式分解, 主成分分析, 峰值信噪比, 数字图像压缩