Kernel PCA for quantization of analog vectors on a pyramid

Abstract

A kernel PCA-based method is proposed for vector quantization that performs a partition of the input space with less distortion than conventional transform coding. The distortion improvement comes at a modest increase in computational complexity and increased entropy of the quantization index stream. The proposed system is especially attractive under severe hardware constraints for which the digital hardware for entropy coding is unavailable. Numerical results are presented to validate the proposed method and to demonstrate the trade-off between distortion and entropy provided by kernel PCA at the source coding level.