2D empirical mode decompositions in the spirit of image compression

The Empirical mode decomposition (EMD) is an adaptive decomposition of the data, as is the Wavelet packet best basis decomposition. This work present the first attempt to examining the use of EMD for image compression purposes. The Intrinsic Mode Function (IMF) and their Hilbert spectra are compared to the wavelet basis and the wavelet packet decompositions expanded in each of its best bases on the same data. By decomposing the signal into basis functions, the waveforms in the signal is represented by the basis and a set of decorrelated discrete values in a vector. A coding scheme is presented where the idea is to decompose the signal into its IMF:s where only the max and min values for each IMF is transmitted. The reconstruction of the IMF in the decoder is done with spline interpolation. We have in the two-dimensional EMD an adaptive image decomposition without the limitations from filter kernels or cost functions. The IMF:s are, in the two-dimensional case, to be seen as spatial frequency subbands, with various center frequency and bandwidth along the image.