you might want to take it further: render an image as the human eye would capture it or even as the human being would perceive it.
There are two ways to interpret this. I'll do both.
Interpretation 1: Render an image that looks perceptually realistic.
At the end of the day, your image still needs to be displayed somewhere. Here's the key: you want to ...
When it comes to perception, there is also the issue of what we are almost blind to (in terms of properties or artifacts), and what we have enforced perception of.
For instance as for sound, you have contrast or frequencies that make you less or not aware of other contents (an old SIGGRAPH paper illustrated how texture can mask mesh resolution), plus all ...
You could try some form of color quantization algorithm, which generally extract the most dominant N colors. The one I've seen referenced most is modified median cut quantization [.pdf], which is based on median cut quantization [.doc]. The benefit to this kind of algorithm is that instead of simply averaging every color in the image, it extracts and ...
According to a review by Legge & Bigelow the arc or degrees of visual angle ($\alpha$) is,
\alpha = 57.3 \times S/D,
where S is height of object and D is distance to object.  $S/D$ is the small angles approximation of $2 \times arctan(S/2D)$ which follows form geometry.
Image 1: the equation comes straight from trigonometric definitions.
There has been quite some research into this using Barten contrast sensitivity function. It is the current formula behind the Dolby Perceptual Quantizer as featured in SMPTE 2084 and HDR10.
This, coupled with a colour appearance model such as the work behind Dr. Mark Fairchild's CIECAM02, can result in very accurate predictions of quantization depth.