|
Interesting thoughts! First thing to mention is that if you look at the code, it uses SSIM, which is a perceptual image metric. Second is that it may be using sRGB, which isn’t a perceptually uniform color space, but is closer to one than linear RGB. I say that simply because most images these days are sRGB encoded. Whether Thera is depends on the dataset. Aren’t Thera’s frequency banks pretty darn close to DCT or Fourier transform already? This is a frequency space decomposition & reconstruction, and their goal is similar to JPG in that it aims to capture the low frequencies accurately, and skimp on the frequencies that matter less, either by being less visible or lead to error (aliasing artifacts). It doesn’t seem entirely accurate to frame this paper as learning in pixel space. As far as perceptual color spaces, yeah that might be worth trying. It’s not clear exactly what the goal is or how it would help, but it might. Thera does use the same color spaces that JPG encoding uses: RGB and YCbCr, which are famously bad. Perceptual color spaces save some bits in the file format, and like frequency space, they are convenient and help with perceptual decisions, but it’s less common to see them used to save work, at least outside of research. Notably, image generation often needs to work in linear color space anyway, and convert to a perceptual color space at the end. For example, CG rendering is all done in linear space, even when using a perceptual color metric to guide adaptive sampling. Another question worth asking is whether in general a neural network already learns the perceptual factors. When it comes to black box training, if the data and loss function capture what a viewer needs to see, then the network will likely learn what it needs and use it’s own notion of perceptual metrics in it’s latent space. In that case, it may not help to use inputs and output that are encoded in a perceptual space, and we might be making incorrect assumptions. In this case with Thera, the paper’s goal may be difficult to pin down perceptually. Doesn’t the arbitrary in ‘arbitrary-scale super resolution’ toss viewing conditions and the notion of an ideal viewer out the window? If we don’t even want to know what the solid angle of a pixel is, we can’t know very much about how they’re perceived. |