Deep Representation and Analysis of Visual Information, Based on the IDP Decomposition

We present contemporary methods for image decomposition analysis in the spectrum domain, based on the Inverse Difference Pyramid (IDP) decomposition. The basic IDP implementations in various aspects of visual information processing and analysis are discussed, in the range from 2D images to third-order tensors. Special attention is paid to the main IDP features, which are compared with those of the famous pyramidal decompositions. The basic IDP modifications are presented: the Reduced Branched IDP, which could be implemented on the basis of various 2D orthogonal transforms (WHT, discrete Fourier transform DFT, DCT, KLT, etc.), and the upgrade to the Adaptive IDP, based on neural networks integration. Special approaches are introduced for the IDP-based decomposition for sequences of correlated images, and some important applications in multidimensional image tensor representation are given; the compression of single and groups of correlated multispectral, multi-view, and computer tomography images; and the faster object search in large image databases. The experimental results obtained by the approaches based on the IDP decomposition, confirm its efficiency which is very high for some image classes. In the conclusions the analysis results and the trends for future investigations and implementations are explained.