Color Bit Depth And Perception In Human Vision
The color space which the human eye perceives has its upper bound at 10 million colors. Anything beyond that is not really distinguishable to the human eye, but will still appear more colorful when being processed by the brain. There is really no exact answer since people have varying distinctions in the way they see color due to the physical characteristics of the eye. Some have great vision while others may have a form of vision defect or impairment.
The value of 10 million distinguishable colors is thus on the high end, but is more lower on the average. You may think that there are really not that many colors if you think only ROYGBIV (Red, Orange, Yellow, Green, Blue, Indigo, Violet) or the colors in a rainbow. The millions of colors are part of a gamut, or combination of others within a spectrum of colors. Gamuts represent the complete range or scope available in a color space.
The CIE 1931 RGB color space and CIE 1931 XYZ color space were the first studies conducted on the visual color spectrum and human vision. This would eventually lead to the development of color management that is important in the field of digital imaging, display, recording and ink technology. It is according to the CIE 1931 XYZ study that estimated human vision can distinguish up to 10 million colors.
Bit Depth
In the study of color the bit depth refers to the number of bits used for each color component in a pixel of a bitmapped image or video frame. The bits are a quantifiable measure that makes up a digital image or frame. The more bits of color in an image, the higher the quality. Images with a high bit depth have a richer and more vibrant color.
Each pixel consists of 3 components that make up color channels. There are 3 types that consist of RGB (Red, Green, Blue). The color bit depth required per channel is 8 bits, for a total of 24 bits. This is also the same as 24-bit color which has a total of 16,777,216 variations (2²⁴). That is much more than the 10 million colors the human eye can distinguish, but nonetheless the colors appear much richer and more vibrant.
A typical display uses the sRGB color space. This makes use of the industry standard of 8-bits per channel (RGB) or 24-bit color. Even though 8-bit seems enough, it still misses out on many colors that are within the CIE 1931 XYZ gamut. This is because you can still have more bits per channel (e.g. 10-bit, 16-bit, 32-bit, etc.). It seems just too much color for any individual person to see, but it really defines higher quality detail in imaging. Thus 8-bits per channel is the required color bit depth in modern electronic consumer displays.
Color Theory
There are 3 primary colors used with digital imaging, namely Red, Green and Blue (RGB). There are also 3 secondary colors, namely Cyan, Magenta and Yellow (CMY).
You can extract CMY from using a combination of primary colors using the following formulas:
G + B = C
R + B = M
R + G = Y
You can also use the secondary colors to get the primary colors:
M + Y = R
C + Y = G
M + C = B
From looking at Adobe’s color wheel, these are opposite colors:
B = -Y
G = -M
R = -C
Therefore:
B = -(R + G) = -Y
G = -(R + B) = -M
R = -(G + B) = -C
That means Blue is the inversion of Red and Green. Green is the inversion of Red and Blue. Red is the inversion of Green and Blue.
Here are the proofs to that statement:
R + G = Y
R + B = M
M + Y = R
M + Y + B = M
B = -Y
(Source: Color Theory)
Human Vision
Physiologically speaking, the human eye is balanced to daylight or sunlight. That would be between 5500K-6500K on the color temperature scale measured in Kelvin (K). However, when colored lights are used the perception of color or the White Balance (WB) changes. These can be artificial light sources, most common at night from incandescent, halogen, fluorescent or LED light bulbs. Our perception of the color white changes, but due to a priori experience we still know it is white but doesn’t appear so.
These are due to changes in the white point. The shifts in the white point is the same as changing the WB in a camera. That is why it is tricky when shooting at night under artificial light sources. Take for example an event shoot which uses floodlights and lamps with different types of light bulbs. These light sources produce varying wavelengths of color in the light. The eye is not used to it so it must compensate for the white point in order to perceive color accuracy. When under halogen lamps, we may see the color appear more orange looking which is between 2800K-3200K. This requires color correction in order to fix the appearance of the image.
Machine Vision
When comparing color in vision between humans and machines, it is like apples to oranges. Machines do not see color at all. We have to program them to be able to see it, and that actually makes them more accurate at seeing colors. This is all based on complex mathematical formulas that process data captured by electronic sensors. Those sensors capture color as wavelengths in the visible light spectrum. They can therefore see more colors than the human eye, and accurately define them.
While humans perceive color with labels like “Red”, “Blue”, etc., machines see color as quantities of RGB. While we say a color is “Red”, to a machine like a computer that is not how it defines color. For a computer “Red” has a wider gamut of colors it measures in quantities of 3 color channels RGB. “Red” to a computer would be “R=255, G=0, B=0”. The representation of white is “R=255, G=255, B=255” and black is “R=0, G=0, B=0”.
When it comes to colors, machines are more precise. While we see color as basic “Red”, it may actually be a combination or variation of RGB (e.g. “R=210, G=7, B=26”). This is because of how computer’s process information. Computers see numbers in terms of digital quantities, while the human perception of color is analog or based on what the eye sees.
Synopsis
Color is really about visual detail and in human perception it helps to identify objects. We understand color not as quantity, the way machines do. Rather we see color as details in an object in the presence of light. When we go to a dark room, without light, we will not be able to see color or see a different perception of it. This is why color changes when the light source is modified. We are used to seeing color in daylight, so when artificial light sources are used our perception of color changes. Yet we still can identify the same colors in different lights only due to experience. This is not the same though for individuals who are color blind or have visual impairment.
Bit depth is used to specify displays for digital imaging systems like cameras, televisions and computer monitors. While the average bit depth of 8-bits per channel is theoretically what humans are capable of, the higher the bit depth the more superior the quality in terms of color. Although we cannot distinguish beyond 10 million colors, we can still see how much better a display’s color looks with higher bit depths. It would be considered overkill for anything higher than 8-bits per channel, but in reality when it comes to media presentations or entertainment value, higher bit depth provides the best visual experience for most people.
