Discrete cosine transform: algorithms, advantages, applications
Discrete cosine transform: algorithms, advantages, applications
The JPEG still picture compression standard
Communications of the ACM - Special issue on digital multimedia systems
Vector quantization and signal compression
Vector quantization and signal compression
Local models and Gaussian mixture models for statistical data processing
Local models and Gaussian mixture models for statistical data processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Pictures: Representation and Compression
Digital Pictures: Representation and Compression
Optimally adaptive transform coding
IEEE Transactions on Image Processing
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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 blocksin a block transform coding scheme. The MPC model partitions the spaceof 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.