The discrete cosine transform is interesting because it's similar to the discrete Fourier transform that we learned about in EE16B (https://inst.eecs.berkeley.edu/~ee16b/fa18/lectures/Lecture14A.pdf) and CS170 (https://cs170.org/assets/notes/Lecture%204.pdf).
The reason why DCT is used instead of DFT for image compression is discussed at https://dsp.stackexchange.com/questions/13/what-is-the-difference-between-a-fourier-transform-and-a-cosine-transform
PongsatornChan
Is this represent an image in frequency space/domain? I don't understand how this formula will be applied to an image.
The discrete cosine transform is interesting because it's similar to the discrete Fourier transform that we learned about in EE16B (https://inst.eecs.berkeley.edu/~ee16b/fa18/lectures/Lecture14A.pdf) and CS170 (https://cs170.org/assets/notes/Lecture%204.pdf).
The reason why DCT is used instead of DFT for image compression is discussed at https://dsp.stackexchange.com/questions/13/what-is-the-difference-between-a-fourier-transform-and-a-cosine-transform
Is this represent an image in frequency space/domain? I don't understand how this formula will be applied to an image.