Understanding Singular Value Decomposition for Image Compression

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  • Thread starter Thread starter Th3HoopMan
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Discussion Overview

The discussion revolves around the concept of Singular Value Decomposition (SVD) and its application in image compression. Participants express varying levels of understanding and seek resources to aid in grasping the theoretical and practical aspects of SVD, particularly in the context of homework assignments.

Discussion Character

  • Exploratory
  • Homework-related

Main Points Raised

  • One participant expresses a lack of understanding of SVD and its application for image compression, seeking simpler resources.
  • Another participant suggests the Wikipedia article as a starting point, highlighting the decomposition of matrices into "important" and "unimportant" components.
  • A participant mentions that they often receive instructions to compute the SVD of specific matrices as part of their coursework.
  • There is a mention of code packages that can perform SVD computations, indicating a potential alternative to manual calculations.
  • One participant notes the requirement to compute SVD by hand for their class, suggesting a focus on manual methods rather than automated solutions.

Areas of Agreement / Disagreement

Participants generally agree on the utility of SVD for image compression but exhibit differing levels of understanding and approaches to learning the concept. There is no consensus on the best resources or methods for mastering SVD.

Contextual Notes

Participants have varying familiarity with SVD, and there are unresolved questions regarding the specific problem-solving processes involved in computing SVD by hand versus using software tools.

Who May Find This Useful

This discussion may be useful for students learning about SVD, particularly in the context of image processing and those seeking resources for understanding matrix decompositions.

Th3HoopMan
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I have no idea what it is, and I really don't know how to go about using SVD on a matrix. All I know is that it could be useful for image compression (okay...?). Are there any resources or videos I can look at that will simplify the problem solving process for this? I've found a few that pertain to the theory but it's usually pretty confusing.
 
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As usual, the wikipedia article has an introduction and many links to resources that go into more detail. The basic idea is to decompose the matrix into "important" and "unimportant" components in a different basis, and then drop the unimportant parts.

Which problem solving process do you mean?
 
Thanks I'll take a look. As for the problems we usually get instructions telling us to compute a SVD of [inset random matrix here].
 
Well, there are code packages that can do that.
 
Yeah i found that out but I have to do it by hand for a class I'm in
 

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