How Important is 10-Bit Really? In: EOSHD Posted March 4, 2018 · Report reply 5 hours ago, cantsin said: You talk about perceptual color depth, No. I am referring to the actual color depth inherent in an image (or imaging system). 5 hours ago, cantsin said: created through dithering, I never mentioned dithering. Dithering is the act of adding noise to parts of an image or re-patterning areas in an image. The viewer's eye blending adjacent colors in a given image is not dithering. 5 hours ago, cantsin said: And even that can't be measured by your formula, because it doesn't factor in viewing distance. Again, I am not talking about dithering -- I am talking about the actual color depth of an image. The formula doesn't require viewing distance because it does not involve perception. It gives an absolute value of color depth inherent in an entire image. Furthermore, the formula and the point of my example are two different things. By the way, the formula can also be used with a smaller, local area of images to compare their relative color depth, but one must use proportionally identical sized areas for such a comparison to be valid. 5 hours ago, cantsin said: Or to phrase it less politely: this is bogus science. What I assert is perfectly valid and fundamental to imaging. The formula is also very simple, straightforward math. However, let's forget the formula for a moment. You apparently admit that resolution affects color depth in analog imaging: 6 hours ago, cantsin said: I see the point that in analog film photography with its non-discrete color values, color depth can only be determined when measuring the color of each part of the image. Naturally, the number of different color values (and thus color depth) will increase with the resolution of the film or the print. Not sure why the same principle fails to apply to digital imaging. Your suggestion that "non-discrete color values" of analog imaging necessitate measuring color in parts of an image to determine color depth does not negate the fact that the same process works with a digital image. The reason why I gave the example of the two RGB pixels is that I was merely trying to show in a basic way that an increase in resolution brings an increase in digital color depth (the same way it happens with an analog image). Once one grasps that rudimentary concept, it is fairly easy to see how the formula simply quantifies digital, RGB color depth. In a subsequent post, I'll give a different example that should demonstrate the strong influence of resolution on color depth.