Spectrum Decomposition for Image/Signal Coding

Abstract

In conventional subband/wavelet image coding, the subband decomposition is performed on the spatial-domain image. Here, we introduce a novel decomposition where the subband decomposition is performed on the global DCT spectrum of the image. That is, the two-dimensional spectrum rather than the image is represented by a sum of basis functions, each weighted by the transform coefficients. The distinct features of this decomposition are analyzed from a transform perspective. This spectral subband decomposition is then used as the basis for a new image coder, building on the condensed wavelet packet (CWP) algorithm. Ironically, this new method is shown to have lower arithmetic complexity than conventional subband/wavelet coders that directly decompose a time or spatial domain signal. Comparisons of the new method against conventional subband/wavelet coders that use the popular 9/7 dyadic decomposition, condensed wavelet packets, and generalized lapped orthogonal transforms, show that the new method has lower complexity and higher compression performance.