A Comparison of Optimal Adaptive and Nonadaptive Transform Coding under a Lapped Orthogonal Projection. R.D. Dony School of Engineering, University of Guelph Guelph, ON, Canada N1G 2W1 Email: rdony@uoguelph.ca PostScript version: nonlinear.ps.Z (820K) Abstract: A number of novel adaptive image compression methods have been developed using a new approach to data representation, a mixture of principal components (MPC). MPC, together with principal component analysis (PCA) and vector quantization (VQ), form a spectrum of representations. The MPC approach still suffers from block effect distortion. While existing lapped transforms eliminate this distortion, they not take into account the need for adaptation on a block-to-block basis. Further, the basis vectors are fixed so they cannot be adapted in any optimal fashion from one image to another. In this paper, a lapped orthogonal projection is used to generate subblocks for both the classic Karhunen-Loève transform (KLT) and the adaptive MPC. The resulting images are free of block effect distortion. Further, the squared error can be reduced. Therefore, both the nonadaptive and adaptive methods under the projection tend to outperform the respective block methods both in terms of subjective criteria and squared error. Lapped projection, principal components, image compression, neural networks