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5. SVD In this question, you will use SVD to compress your image. Take a picture of yourself and load it using a programming language of your choosing. For Matlab and Python, you can respectively look into functions imread and open from Python Imaging Library (PIL).

5. SVD In this question, you will use SVD to compress your image. Take a picture of yourself and load it using a programming language of your choosing. For Matlab and Python, you can respectively look into functions imread and open from Python Imaging Library (PIL).

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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5. SVD In this question, you will use SVD to compress your image. Take a picture of yourself and load it using a programming language of your choosing. For Matlab and Python, you can respectively look into functions imread and open from Python Imaging Library (PIL). (a) After loading your image, turn it into gray-scale. Write a short script for computing its truncated SVD. You can use the inbuilt function svd for both Matlab and Python (in the Numpy library). Start with rank r = 2 and go up by powers of 2, to r = 64. Show the resulting images. (b) Comment on the performance of the truncated SVD. State how much storage is required as a function of r and matrix dimensions and compare it with the storage required for the original picture. (c) Now do the same but keep the colors, i.e., don't turn your image into gray-scale. Your code for this part should output colored compressions of your original image that (Hint: consider performing SVD on each RGB color band separately and then combine the results).
Transcribed Image Text:5. SVD In this question, you will use SVD to compress your image. Take a picture of yourself and load it using a programming language of your choosing. For Matlab and Python, you can respectively look into functions imread and open from Python Imaging Library (PIL). (a) After loading your image, turn it into gray-scale. Write a short script for computing its truncated SVD. You can use the inbuilt function svd for both Matlab and Python (in the Numpy library). Start with rank r = 2 and go up by powers of 2, to r = 64. Show the resulting images. (b) Comment on the performance of the truncated SVD. State how much storage is required as a function of r and matrix dimensions and compare it with the storage required for the original picture. (c) Now do the same but keep the colors, i.e., don't turn your image into gray-scale. Your code for this part should output colored compressions of your original image that (Hint: consider performing SVD on each RGB color band separately and then combine the results).
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