This paper proposes a multiresolution Matching Pursuit decomposition of natural images. Matching Pursuit is a greedy algorithm that decomposes any signal into a linear expansion of waveforms taken from a redundant dictionary, by iteratively picking the waveform that best matches the input signal. Since the computational cost rapidly grows with the size of the signal, we propose a multiresolution strategy that, together with an efficient dictionary, significantly reduces the encoding complexity while still providing an efficient representation. Such a decomposition is perceptually very effective at low bit rate coding, thanks to similiarities with the Human Visual System information processing.
Silvestro Micera, Daniela De Luca
Florian Frédéric Vincent Breider, Myriam Borgatta
Giuseppe Carleo, Sofia Vallecorsa