Abstract
The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multi-layered representation technique for images. An image is parsed into a superposition of coherent layers: smooth-regions layer, textures layer, etc. The multi-layered decomposition algorithm consists in a cascade of compressions applied successively to the image itself and to the residuals that resulted from the previous compressions. During each iteration of the algorithm, we code the residual part in a lossy way: we only retain the most significant structures of the residual part, which results in a sparse representation. Each layer is encoded independently with a different transform, or basis, at a different bitrate; and the combination of the compressed layers can always be reconstructed in a meaningful way. The strength of the multi-layer approach comes from the fact that different sets of basis functions complement each others: some of the basis functions will give reasonable account of the large trend of the data, while others will catch the local transients, or the oscillatory patterns. This multi-layered representation has a lot of beautiful applications in image understanding, and image and video coding. We have implemented the algorithm and we have studied its capabilities.
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Meyer, F.G., Averbuch, A.Z., Coifman, R.R. (2001). Multi-Layered Image Representation. In: Petrosian, A.A., Meyer, F.G. (eds) Wavelets in Signal and Image Analysis. Computational Imaging and Vision, vol 19. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9715-9_10
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DOI: https://doi.org/10.1007/978-94-015-9715-9_10
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