1. Image Compression Using An Adaptation of the ART Algorithm
Craig Weidert
December 12, 2006
CS152 - Neural Networks

2. Background I would like to use neural networks to compress images.
Used 48 by 48 pixel 8-bit grayscale bitmaps
Implemented in Python
I tried two methods

3. Attempt #1 (Failure) Standard NN w/ Backprop Gameplan:
Train network on image
Save weights of network
Restore network, run through co-ordinates to restore image
Inputs: x and y co-ordinates of pixels
Desired Output: grayscale value of pixels
Used two hidden 10 wide logsig layers

4. Results / Problems Difficulty in training Not very faithful
Took a long time
Grayout problem
Inconsistent
Not very faithful
Lower right hand corner

5. Attempt #2 (Relative Success)
Use scheme similar to k-means and adaptive resonance theory
Gameplan:
Use network to cluster pixels into “prototypes” based on position, color
Save prototypes
Load prototypes, for each pixel find closest prototype and use that color

6. Algorithm For each pixel in the image, create sample (x, y, c)
Iterate through samples
If there are no prototypes, make one
If there is a prototype within a tolerable distance of the sample, lump in w/ closest
If all prototypes are far away, make new prototype
Save the prototypes, truncating as necessary

7. Results Ran much faster
At the point where you could tolerate the error, often no longer compression
However, achieved a very cool mosaic effect

10. Future Work Differing image sizes
Scalability questions
Different comparisons for samples, prototypes
Different weightings
Different metrics (MSE)?
Changing Algorithm entirely