In the last decade we observed an increasing interaction between data compression and sparse signals approximations. Sparse approximations are desirable because they compact the energy of the signals in few elements and correspond to a structural simplification of the approximated signals. In the first part of this dissertation we consider the low bit rate image coding problem. The state of the art in image compression is to use two-dimensional (2-D) wavelets. The main advantage of wavelet bases lie in their multiscale nature and in their ability to sparsely represent functions that are piecewise smooth. Their main problem on the other hand, is that 2-D wavelets are not able to deal with the natural geometry of images, i.e. they cannot sparsely represent objects that are smooth away from regular submanifolds. We propose an approach based on building a sparse approximation of the edge part of images in a redundant geometrically inspired library of functions, followed by suitable coding techniques. The edge-oriented dictionary is built by anisotropically scaling, orienting and bending a generating function. The new bending transform generates elementary waveforms that efficiently approximate the edges; with few waveforms we are able to approximate long edges. The sparse approximations over the redundant dictionary are computed using a sub-optimal greedy strategy, as indeed finding the best m-terms non-linear approximations in general dictionaries is a NP-hard problem. Recently there has been an intense activity in the field of sparse approximations with redundant dictionaries, largely motivated by the practical performances of algorithms such as Matching Pursuit and Basis Pursuit. However, most of the theoretical results obtained so far are valid only for the restricted class of incoherent dictionaries. The second part of the dissertation investigates a new class of overcomplete dictionaries, called block incoherent dictionaries, where coherence can be arbitrarily large. We show that a simple greedy algorithm can correctly identify stable subdictionaries (called blocks) and demonstrate how one can use the extra coherence freedom for approximation purposes. With the aim of improving the sparse approximations over redundant dictionaries obtained with greedy algorithms, in the last part we develop a new decomposition algorithm characterized by the computational cost of MP and global optimization properties typical of the re-weighted minimum norm algorithms such as FOCUSS. We introduce the concept of sub-dictionary, a subset of the redundant dictionary which depends on the signal to be approximated, and we propose a sub-dictionary selection algorithm based on greedy techniques. The low computational cost greedy algorithm selects a sub-dictionary that contains the solution of more complex algorithms, which are utilized to obtain the approximation over the sub-dictionary. In the first part we studied image and video sparse approximation, however the concepts we present in the second part of the dissertation are general and can be applied to any kind of structured signal.