Multilayered image representation: application to image compression

• Published in: IEEE transactions on image processing Vol. 11; no. 9; pp. 1072 - 1080
• Main Authors: Meyer, F.G., Averbuch, A.Z., Coifman, R.R.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Basis functions; Bit rate; Compressing; Discrete cosine transforms; Discrete wavelet transforms; Exact sciences and technology; Image coding; Image compression; Image processing; Image reconstruction; Image representation; Information, signal and communications theory; Libraries; Nonhomogeneous media; Pursuit algorithms; Representations; Signal processing; Surface layer; Telecommunications and information theory; Texture; Video coding; Performance evaluation; Image coding; Image processing; Wavelet transformation; Image representation; Image compression; Algorithm; Adaptive method; Cosine transform
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2002.802527

The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multilayered representation technique for images. An image is parsed into a superposition of coherent layers: piecewise smooth regions layer, textures layer, etc. The multilayered 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 multilayer 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 multilayered 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.