Adaptive Split-and-Merge for Image Analysis and Coding
Riccardo Leonardi,Murat Kunt +1 more
- Vol. 0594, pp 2-9
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TLDR
In this paper, an approximation algorithm for two-dimensional (2-D) signals, e.g. images, is presented by partitioning the original signal into adjacent regions with each region being approximated in the least square sense by a 2-D analytical function.Abstract:
An approximation algorithm for two-dimensional (2-D) signals, e.g. images, is presented. This approximation is obtained by partitioning the original signal into adjacent regions with each region being approximated in the least square sense by a 2-D analytical function. The segmentation procedure is controlled iteratively to insure at each step the best possible quality between the original image and the segmented one. The segmentation is based on two successive steps: splitting the original picture into adjacent squares of different size, then merging them in an optimal way into the final region configuration. Some results are presented when the approximation is performed by polynomial functions.read more
Citations
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Proceedings ArticleDOI
Image Data Compression
TL;DR: Directional sensitivity of the neurones in the visual pathway combined with the separate processing of contours and textures has led to a new class of coding methods capable of achieving compression ratios as high as 100:1.
Nonlinear multiscale methods for estimation, approximation, and representation of signals and images
TL;DR: This work develops computationally efficient procedures for solving certain restoration problems in 1-D including the discrete versions of the total variation regularized problem and the constrained total variation minimization problem and proposes new algorithms for efficiently finding the best representation in a multitree dictionary.
Proceedings ArticleDOI
Low bit rate image compression with orthogonal projection pursuit neural networks
TL;DR: A new multiresolution algorithm for image compression based on projection pursuit neural networks is presented that shows excellent performance both in terms of peak S/N ratio and subjective image quality.
High compression image and image sequence coding
TL;DR: Recent progress on some of the main avenues of object-based methods in picture coding make use of contour-texture modeling, new results in neurophysiology and psychophysics and scene analysis.
Posted Content
Graph Wedgelets: Adaptive Data Compression on Graphs based on Binary Wedge Partitioning Trees and Geometric Wavelets.
TL;DR: Graph wedgelets as discussed by the authors is a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary wedge partitionings of a graph.
References
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