Theoretical Results for Applying Neural Networks to Lossless Image Compression
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1994-03-01T00:00:00Z
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Abstract
The ability to employ neural networks to the task of image compression has been pointed out in recent research. The pre-dominant approach to image compression is centered around the backpropagation algorithm to train on overlapping frames of the original picture. Several deficiencies can be identified with this approach: First, no potential time bounds are provided for compressing images. Second, utilizing backpropagation is difficult due to its computational complexity. To overcome these shortcomings we propose a different approach by concentrating on a general class of 3-layer neural networks of 2(N+1) hidden units. It will be shown that the class ${\cal N}^{*}$ can uniquely represent a large number of images, in fact, growth of this class is larger than exponential. Instead of training a network, it is automatically constructed. The construction process can be accomplished in ${\cal O}_{Worst}(n) = n^{4} - n^{2}$ time, where $n$ is the image size. Obtainable compression rates (lossless) exceed 97\% for square images of size 256.