When I was 10, I got up early on a Saturday for the Smurfs. Now, I get up early for Sparsity. No commercial interruptions. All that's missing is some Lucky Charms.
Thank you Steve. Your RU-vid videos are a great balance of accessibility of quick bite-sized concepts without the lowering of discussion to menial examples.
Thank you very much for these amazing and well detailed videos, I'm actually working on my Master's degree thesis about cognitive radio and these videos helped a lot in the compressed sensing chapter!
Thank you Professor Steve. Just what I was looking for! Your tutorials have helped me in my masters in Control engineering, and this new series is a helping hand at the start of my PhD.
Holy shit man, I just discovered your channel and it feels like I have found a gold mine! There are soo many useful videos that I don't even know where to begin. Is there any way to support your work so you can continue creating this wonderful content? Patreon, Paypal, Donations?
I just wish this was available 30 years back when I worked on video compression. Great material and well presented indeed. But as far as I recall JPEG relied on DCT, not DFT.
7:30 When the metalhead in you kicks in \\m// :) One question, what is a quantitative measure of sparsity? Like how much percentage of elements should be zero to count a matrix as sparse?
I suspect sparsity and structure might be the same thing. I think that translates to a high degree of linear independence being more informative. Then information is something which is less dependent on circumstance.
Thank you for the video! very helpful! is it possible that some basis gives us a more sparse result than others? Like here the DFT may give us 10 non-zero entries of a 1mil entry image, but what happens if we use a different transform that gives us 5 or 20 non-zero entries? Does that make a difference and is it in our interest to look for transforms that keep increasing the sparsity?