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Results

Module by: Genaro Picazo. E-mail the author

Summary: Results for our ELEC 301 Project for Fall 2003. By: Chris Omidiran, Genaro Picazo, Ian Wells, Daniel Wu

Data

Here are the three test images, at multiple levels of compression in either dictionary. (Note that 0% compression does not necessarily lead to full recovery - this is a problem inherent in the dct basis. It would likely take more than twice as much data to represent the original image nearly perfectly.)

images.JPG

We decided to run our algorithms on the three images with a non-redundant basis, just to see how the compressions compared.

controlimages.jpg

Data Analysis

Based on preliminary tests, we concluded that orthogonal matching pursuit produced approximations very similar to those generated by basis pursuit in a fraction of the time. However, varying the basis in our over-complete basis produced dramatic changes in our compressions, as did the inherent visual characteristics of the source image. These observations suggest that a wide spectrum of interesting results from orthogonal matching pursuit can be obtained by varying both the dictionaries and the original image. Our results show that for the three images, the basis that give the best representation with the most compression is the DFT-DCT basis, although compression in this manner often results in an unfortunate spectral artifact on images that exhibit sharp changes in color, a product of the DFT basis. The Dirac basis seemed best suited to picking out "lines" in the image, as we predicted. Interestingly, and as shown by the following graphs, the DCT basis dominated both overcomplete dictionaries, accounting for a vast majority of the compressed image - but the DFT/Dirac based vectors, however few there were, managed to make very significant changes to the overall apprearance of the final image.

We also analyzed how frequently vectors from either basis were being selected, ie which basis was the most influential.

bargraph.jpgDFT-DCT.jpgDIRAC-DCT.jpg

(4 = 98% compressed, 16 = 87%, 64 = 50%, 128 = 100%)

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Definition of a lens

Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

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'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

Definition of a lens

Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

| External bookmarks