Learn How to Efficiently Compress Color Images with SVD in Matlab

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SUMMARY

This discussion focuses on efficiently compressing color images using Singular Value Decomposition (SVD) in Matlab. It confirms that a color image can be treated as three separate grayscale images, allowing for individual compression of each matrix. The conversation highlights the need for specific resources that demonstrate this process without requiring conversion to grayscale first. Users are encouraged to explore SVD techniques applicable to color images directly.

PREREQUISITES
  • Understanding of Singular Value Decomposition (SVD)
  • Familiarity with Matlab programming environment
  • Basic knowledge of image processing concepts
  • Experience with matrix manipulation in Matlab
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  • Research "SVD image compression techniques in Matlab"
  • Explore "Matlab functions for matrix manipulation"
  • Learn about "color image representation in terms of grayscale"
  • Investigate "advanced image processing techniques in Matlab"
USEFUL FOR

Image processing professionals, Matlab developers, and anyone interested in optimizing color image compression techniques using SVD.

matqkks
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Are there any resources which use Matlab to image compress a colour image using SVD? I can only find information where I need to convert to gray scale first.
 
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A color image is essentially 3 grayscale images, right? Can you just compress each of the three matrices individually using the methods available for grayscale images?
 

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