Image Compression Using KLT, Wavelets and an Adaptive Mixture of Principal Components Model

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Abstract

In this paper, we present preliminary results comparing the nature of the errors introduced by the mixture of principal components (MPC) model with a wavelet transform and the Karhunen Loève transform (KLT) for the lossy compression of brain magnetic resonance (MR) images. MPC, wavelets and KLT were applied to image blocks in a block transform coding scheme. The MPC model partitions the space of image blocks into a set of disjoint classes and computes a separate KLT for each class. In our experiments, though both the wavelet transform and KLT obtained a higher peak signal to noise ratio (PSNR) than MPC, according to radiologists, MPC preserved the texture and boundaries of gray and white matter better than the wavelet transform or KLT.

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