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Low bit rate image compression with orthogonal projection pursuit neural networks

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4 Author(s)
Safavian, S.R. ; Cellular Inst., LCC Inc., Arlington, VA, USA ; Rabiee, H.R. ; Fardanesh, M. ; Kashyap, R.

A new multiresolution algorithm for image compression based on projection pursuit neural networks is presented. High quality low bit-rate image compression is achieved first by segmenting an image into regions of different sizes based on perceptual variation in each region and then constructing a distinct code for each block by using the orthogonal projection pursuit neural networks. This algorithm allows one to adaptively construct a better approximation for each block by optimally selecting the basis functions from a universal set. The convergence is guaranteed by orthogonalizing the selected bases at each iteration. The coefficients of the approximations are obtained by back-projection with convex combinations. Our experimental results shows that at rates below 0.5 bits/pixel, this algorithm shows excellent performance both in terms of peak S/N ratio and subjective image quality

Published in:

Neural Networks,1997., International Conference on  (Volume:3 )

Date of Conference:

9-12 Jun 1997