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Image compression using a feedforward neural network

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2 Author(s)
R. Setiono ; Dept. of Inf. Syst. & Comput. Sci., Nat. Univ. of Singapore, Singapore ; Guojun Lu

We present an image compression technique using a feedforward neural network. The neural network has three layers: one input layer, one hidden layer and one output layer. The inputs of the neural network are original image data, while the outputs are reconstructed image data which are close to the inputs. If the amount of data required to store the hidden unit values and the connection weights to the output layer of the neural network is less than the original data, compression is achieved. In our experiments, we achieved a compression ratio of about 10 with good reconstructed image quality. The neural network construction algorithm we used has an attractive feature that each addition of a hidden unit to the network will always improve the image quality. Thus our compression scheme is flexible in the sense that the user can trade between image quality and compression ratio depending on the application requirements

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994