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A New Image Compression Technique Based on Combining Feedforward Neural Networks and Discrete Cosine Transform

Research Authors
P.E. William, T.K. Abdel-Hamid, M.M. Doss and H. Selim
Research Member
Research Department
Research Year
2004
Research Journal
Proc. 4th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2004), Newcastle, U.K.
Research Rank
3
Research_Pages
pp. 448-451
Research Abstract

In this paper, we propose an algorithm for the application of one-hidden layer Feedforward Neural Network (OHL-FNN) to image compression. The algorithm combines OHL-FNN with Discrete Cosine Transform (DCT), here, the neural network learning algorithm performs the compression in a spectrum domain of DCT coefficients, i.e., the OHL-FNN approximates only the DCT coefficients representing the high detailed part of the image, Network parameters are stored in order to recover the image. Results, compared with baseline JPEG algorithm, demonstrate that the new algorithm dramatically increase compression for a given quality; conversely it increases image quality for a given compression ratio.