image provider

Image Compressor


This essay does not describe an existing computer program, just one that should exist. This essay is about a suggested student project in Java programming. This essay gives a rough overview of how it might work. I have no source, object, specifications, file layouts or anything else useful to implementing this project. Everything I have prepared to help you is right here.

This project outline is not like the artificial, tidy little problems you are spoon-fed in school, when all the facts you need are included, nothing extraneous is mentioned, the answer is fully specified, along with hints to nudge you toward a single expected canonical solution. This project is much more like the real world of messy problems where it is up to you to fully the define the end point, or a series of ever more difficult versions of this project and research the information yourself to solve them.

Everything I have to say to help you with this project is written below. I am not prepared to help you implement it; or give you any additional materials. I have too many other projects of my own.

Though I am a programmer by profession, I don’t do people’s homework for them. That just robs them of an education.

You have my full permission to implement this project in any way you please and to keep all the profits from your endeavour.

Please do not email me about this project without reading the disclaimer above.

There are a number of schemes to compress images so that they don’t take up as much space as they might otherwise. Schemes include: Your task is to see if you can come up with something more efficient. Ideally it will have the nice property than jpg does, such that when you blow up images they still look reasonable.

Here is the gist of an idea of how you might proceed.

  1. First detect sharp edges. They break the image into regions bounded by sharp edges.
  2. Define the boundaries using spline interpolation or other technique that tends to form nice smooth round edges.
  3. Consider each region separately. Split it into three images, a red, green and blue. Now draw a contour map of the region using the intensity of each colour as the Z-axis height.
  4. You will see concentric circles around each point representing a local maximum or local minimum. At the center point to your list of sample points, along with its intensity value.
  5. Add some more sample points around the boundary.
  6. Now recreate the original image smoothly interpolating just from your sample points.
  7. Compute the error at each point.
  8. Add the worst error point to the sample set.
  9. Recreate the image with the new interpolation information.
  10. Repeat with the other two colour separations.
  11. Repeat until your error is sufficiently small or your sample set is as large at the user is willing to tolerate.
I would presume this technique will not do as well as those with a mathematical foundation, but it would fun to try. Go after the niche where you are willing to spend a lot of time compressing for a small gain.

Alternatively, you might read up on how existing compression schemes work and see if you can find some tweak to make them a tiny bit better.


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