基于分块PCA算法的医学图像压缩

S. T. Lim, D. Yap, N. A. Manap
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引用次数: 15

摘要

PCA算法可以用来辅助图像压缩。本文对两种处理图像块信息的扩展pca算法进行了测试和比较。第一种算法被称为逐块PCA,其中将一般PCA算法应用于图像的每个块。在第二种算法-块到行PCA中,所有块信息首先被连接到行,然后在转换后的矩阵上应用一般PCA算法。本研究采用数字眼底图像作为输入图像。使用新导出的压缩比,结果表明,块到行PCA在图像质量和压缩率方面优于块到块PCA。在相同的块大小和压缩比下,块到行PCA比块到行PCA具有更高的PSNR。块对块PCA在块大小为16、压缩比低至0.25的情况下,对重构图像有明显的块效应,而块大小为32、压缩比高达0.90的块对行PCA在重构图像上没有明显的失真。
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Medical image compression using block-based PCA algorithm
PCA algorithm can be employed to aid in image compression. In this paper, two extended-PCA algorithms that manipulate the block information of the image are tested and compared. The first algorithm is termed as block-by-block PCA in which general PCA algorithm are applied on each block of the image. In the second algorithm- the block-to-row PCA, all block information are first concatenated into row before general PCA algorithm is then applied on the transformed matrix. Digital fundus image is used as the input image in this work. Using the newly-derived compression ratios, the result shows that block-to-row PCA outperforms block-by-block PCA in terms of image quality and compression rate. At equal block size and compression ratio, block-to-row PCA can achieve higher PSNR than block-to-block PCA. Blocking effects are discernible on the reconstructed image using block-to-block PCA with block size = 16 and compression ratio as low as 0.25 while no apparent distortion are seen on the reconstructed image using block-to-row PCA with block size = 32 and compression ratio as high as 0.90.
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