Principal Component Analysis (PCA) is well known for its capability of dimension reduction and data compression. However, when using PCA for compressing/reconstructing images, images need to be recast to vectors. The vectorization of images makes some correlation constraints of neighboring pixels and spatial information lost. To deal with the drawbacks of the vectorizations adopted by PCA, we used small neighborhoods of each pixel to form compounded pixels and use a tensorial version of PCA, called TPCA (Tensorial Principal Component Analysis), to compress and reconstruct a compounded image of compounded pixels. Our experiments on public data show that TPCA compares favorably with PCA in compressing and reconstructing images. We also show in our experiments that the performance of TPCA increases when the order of compounded pixels increases.
翻译:主要成分分析(PCA)因其尺寸减少和数据压缩的能力而广为人知,然而,在使用五氯苯甲醚压缩/再制造图像时,图像需要重新刻录为矢量。图像的传导性使相邻像素和空间信息丢失具有一些相关限制。为了处理五氯苯甲醚采用的矢量化的缺点,我们利用每个像素的小区块形成复合像素,并使用称为TPCA(感官主构件分析)的气压版五氯苯甲醚,以压缩和重塑复合像素的复合图像。我们对公共数据的实验表明,在压缩和重塑图像方面,TPCA的性能优于五氯苯甲醚。我们在实验中还表明,当复合像素的顺序增加时,TPCA的性能会提高。