基于奇异值分解和Contourlet变换的图像压缩算法

随着互联网的飞速发展，产生大量的图像信息。为了减小存储并提高图像质量，故提出了一种基于奇异值分解和Contourlet变化结合的有损图像压缩算法。该算法先对图像进行奇异值分解，根据奇异值对图像信号的贡献，选取适当的奇异值，来实现图像压缩，再对图像进行Contourlet 变换和量化，实现图像二级压缩。将该算法和图像奇异值分解直接压缩算法、Contourlet变换压缩算法进行实验比较，试验结果表明，该算法比图像奇异值分解直接压缩算法、Contourlet变换压缩算法有更好的性能，在同样的压缩比的情况下能获得更高的峰值信噪比和SSIM。

Lossy image compression using SVD and Contourlet transform

With the rapid development of Internet, image information is growing. In order to reduce the storage and improve the image quality, the paper proposes a new image compression algorithm that combines SVD and Contourlet transform. The image is decomposed by singular value decomposition. According to the contribution of singular value to the image signal, the appropriate singular value is selected to realize image compression. Then the image is compressed again using Contourlet transform compression algorithm. Compared with the results of singular value decomposition algorithm and Contourlet transform compression algorithm, the results show that the proposed algorithm has better performance than singular value decomposition algorithm and Contourlet transform compression algorithm. The proposed algorithm can obtain higher peak signal to noise ratio and SSIM in the same compression ratio.