Sharp has a prototype 5-colour display (RGBYC) that is simply gorgeous, and will make people who just bought their 4-colour (RGBY) display jealous.
If you make your image’s histogram flat, or its cumulative histogram have a roughly 45-degree slope (these are equivalent), its contrast will be higher and people will like how it looks.
However, matching histograms between images, while it can make one image have the colour palette of another, doesn’t produce pleasing results.
If you calculate the x and y gradients of a large volume of natural images (pictures of the real world), there is a large peak at a gradient value of zero. This implies that most of the natural world is composed of large homogenous surfaces instead of having lots of edges.
Further, the distribution of those gradients, both in x and y, falls off very quickly, implying that those edges that exist are mostly low contrast.
Finally, the distribution of those gradients is symmetric about 0, meaning that surfaces are mostly on top of the same background, like a window in a house.
If you take the power spectrum of an image, on average it follows the power law: A = 1 / fβ. If you plot these spectra on a logarithmic scale, you get a straight line with a slope of β.
Human beings are most sensitive to slopes of β = 2.8 to 3.2, but the average spectral slope of images is about 2.0. This implies that we’re tuned to see things that are really coarse, even though the average scene isn’t that way.
If you control for orientation, you figure out what an image is a picture of very effectively just by looking at their 2D power spectra!
PCA, Principal Component Analysis, is simply a method of computing the eigenvalues and eigenvectors of a set of data. This lets you figure out (by looking at which eigenvalue is the largest) where most of the variance of a set of data is; by sorting the values, you can figure out what your most important components are.