Lossy Image Compression Using PCA and Contourlet Transform
School of Science, Wuhan University of Technology, Wuhan 430070, China
With the rapid development of Internet, image information is growing. It requires a lot of image storage and transmission. In order to reduce the storage and get better image quality, image compression algorithm is studied. The paper proposes a new image compression algorithm that combines principal component analysis (PCA) and Contourlet Transform (CT). Because PCA has good image quality, but the compression ratio is low, and CT compression algorithm has high compression ratio and good PNSR value. The image is decomposed by PCA. The image data is divided into blocks, and each block is used as a sample vector, then select covariance matrix of k larger eigenvalues corresponding eigenvector to realize image compression. Then the image is compressed again using CT compression algorithm. Compared with the results of JEPG2000 and CT compression algorithm, the results show that the proposed algorithm has better performance than JEPG2000 and CT compression algorithm. In the same compression ratio, PNSR value of proposed algorithm is about 3dB higher than that of JEPG2000, and 2dB higher than that of CT compression algorithm.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.