1 November 1992 Unsupervised orthogonalization neural network for image compression
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Proceedings Volume 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods; (1992) https://doi.org/10.1117/12.131602
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States
Abstract
In this paper, we present a unsupervised orthogonalization neural network, which, based on Principal Component (PC) analysis, acts as an orthonormal feature detector and decorrelation network. As in the PC analysis, this network involves extracting the most heavily information- loaded features that contained in the set of input training patterns. The network self-organizes its weight vectors so that they converge to a set of orthonormal weight vectors that span the eigenspace of the correlation matrix in the input patterns. Therefore, the network is applicable to practical image transmission problems for exploiting the natural redundancy that exists in most images and for preserving the quality of the compressed-decompressed image. We have applied the proposed neural model to the problem of image compression for visual communications. Simulation results have shown that the proposed neural model provides a high compression ratio and yields excellent perceptual visual quality of the reconstructed images, and a small mean square error. Generalization performance and convergence speed are also investigated.
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Lurng-Kuo Liu, Panos A. Ligomenides, "Unsupervised orthogonalization neural network for image compression", Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); doi: 10.1117/12.131602; https://doi.org/10.1117/12.131602
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