Contrast is the difference in brightness or color that helps an object stand out from its background. Human eyes are better at noticing differences in brightness than seeing the actual level of brightness. This means that how something looks can stay similar even if the lighting changes a lot.

The highest level of contrast in an image is called the contrast ratio or dynamic range. When an image has a contrast ratio close to the maximum possible for the medium used, contrast is conserved. This means that making one part of the image more contrasted will cause another part to become less contrasted. For example, making an image brighter increases contrast in darker areas but decreases it in brighter areas. The opposite happens when making an image darker. Bleach bypass is a technique that reduces contrast in the darkest and brightest parts of an image while increasing contrast in areas that are in the middle of the brightness range.

Biological contrast sensitivity

Campbell and Robson (1968) found that the human eye's ability to detect contrast, called the contrast sensitivity function, follows a shape similar to a band-pass filter. This shape has a peak sensitivity around 4 cycles per degree (cpd or cyc/deg), with sensitivity decreasing on both sides of the peak. This can be seen by changing the distance from a "sweep grating," which is a pattern of bars that change from high to low contrast along the bars and from narrow (high spatial frequency) to wide (low spatial frequency) bars across the width of the grating.

The high-frequency cut-off shows the limits of the eye's ability to see fine details and is usually about 60 cpd. This is also connected to how closely the photoreceptor cells in the retina are packed together. A denser arrangement of these cells allows the eye to detect finer patterns.

The drop-off at low frequencies happens because of lateral inhibition in retinal ganglion cells. Each retinal ganglion cell has a central area where light either increases or decreases the cell's activity and a surrounding area where light has the opposite effect.

One example of this is when blue light is shown against a white background. In the periphery, the blue light appears less intense, creating a yellow appearance around it. This yellow comes from the reduction of blue light in the surrounding area. Since white light minus blue light includes red and green, these colors mix to form yellow.

For example, in graphical computer displays, contrast is influenced by the characteristics of the image file and the display itself, including its settings. On some screens, the angle between the screen and the viewer's line of sight also affects contrast.

Quantifications

There are many ways to define contrast. Some definitions include color, while others do not. A Russian scientist named N. P. Travnikova said, "Having so many different ideas about contrast is very confusing. It makes it harder to solve real-world problems and compare results from different studies."

Different definitions of contrast are used in different situations. Here, we will look at luminance contrast as an example. These formulas can also be used for other types of measurements. In many cases, contrast is described as a ratio. This is because a small difference in brightness is not important if the average brightness is very high, but the same small difference is more important if the average brightness is low (this is related to the Weber–Fechner law). Below are some common definitions of contrast.

Weber contrast is calculated by taking the difference between the brightness of a feature and the brightness of the background, then dividing that difference by the brightness of the background. This measure is sometimes called the Weber fraction because it is the key part of Weber's Law. Weber contrast is often used when small features appear on a large, uniform background, where the average brightness is close to the background brightness.

Michelson contrast is commonly used for patterns that have both bright and dark parts that cover similar areas (such as sine-wave gratings). Michelson contrast is calculated by taking the difference between the highest and lowest brightness levels and dividing that by the sum of the highest and lowest brightness levels. The denominator represents twice the average of the highest and lowest brightness levels.

This type of contrast is useful for measuring contrast in repeating patterns, like the function f(x). It is also known as the modulation of a repeating signal. Modulation measures how much the brightness (or difference) of the pattern stands out from the average brightness. If the modulation is zero, the pattern has no contrast. If two patterns have the same average brightness, the one with a higher modulation has more contrast.

Root-mean-square (RMS) contrast does not depend on how the brightness changes across the image or where the changes occur. RMS contrast is calculated by finding the standard deviation of the brightness values in the image. The standard deviation is the average of how much each brightness value differs from the average brightness. The image is assumed to have brightness values between 0 and 1. This is the same as dividing the standard deviation by the average brightness (with or without adjusting the brightness values).

Other ways to measure contrast, such as the haziness metric, have been developed to describe contrast in a way that is easier to understand. These methods focus on how well different parts of an image can be told apart.

Contrast sensitivity

Contrast sensitivity is a measure of how well the eyes can tell the difference between light and dark areas in a still image. It changes with age, reaching the highest level around the age of 20 for images with a detail level of about 2–5 cycles per degree (cpd). As people get older, their ability to see these details decreases. Conditions such as cataracts and diabetic retinopathy also lower contrast sensitivity. In the sweep grating image, when viewed from a normal distance, the middle bars look the longest because they match the best detail level. When viewed from a far distance, the longest visible bars shift to the wider bars, which now match the detail level of the middle bars when viewed up close.

Visual acuity is a common way to check overall vision. However, even if someone has normal visual acuity, poor contrast sensitivity can still make it harder to see clearly in real-life situations. For example, some people with glaucoma may have 20/20 vision on tests but have trouble with tasks like driving at night.

Contrast sensitivity describes how well the eyes can see bright and dark parts of a still image. Visual acuity refers to the smallest angle at which two points can be seen as separate, using a high-contrast image shown directly to the center of the retina. When an eye doctor tests visual acuity with a Snellen chart, the letters are shown in high contrast, such as black letters on a white background. A contrast sensitivity test might show difficulty seeing images with lower contrast, like those on the Pelli–Robson chart, which uses uniform-sized, increasingly pale gray letters on a white background.

To test contrast sensitivity, doctors may use charts with images of different contrast levels and detail levels. These charts often include parallel bars of varying width and contrast, called sine-wave gratings. The width of the bars and the space between them represent the detail level, measured in cycles per degree (cpd).

Studies show that the eyes are best at seeing images with a detail level of 2–5 cpd. Sensitivity drops for images with lower or higher detail levels. The maximum detail level the human eye can see is about 60 cpd. Reading small letters requires a detail level of about 18–30 cpd. Contrast threshold is the smallest amount of contrast a person can see. Contrast sensitivity is usually shown as the reciprocal of this threshold (1 ÷ contrast threshold).

A contrast sensitivity test can create a graph, called a contrast sensitivity function (CSF), showing detail level on the horizontal axis and contrast threshold on the vertical axis. This graph shows the normal range of contrast sensitivity. If a person’s results fall below the normal range, it means their contrast sensitivity is reduced. Some graphs include "contrast sensitivity acuity equivalents," where lower acuity values appear under the curve. People with normal vision but reduced contrast sensitivity may have trouble with tasks like driving at night or climbing stairs, where contrast is low.

Recent studies show that the retina detects patterns with medium detail best because of how nerve cells are arranged. In these patterns, the bright parts are seen by the center of the nerve cells, while the dark parts are seen by the edges of the cells. This is why low- and high-detail patterns can confuse the eyes. Other factors, like how the eyes adjust to light, also affect how well the eyes see these patterns.

Poor contrast sensitivity can be caused by many issues, such as retinal problems like age-related macular degeneration (ARMD) or amblyopia, lens problems like cataracts, and brain-related issues like stroke or Alzheimer’s disease. Because many different causes can lead to reduced contrast sensitivity, these tests help track eye problems but are less useful for finding diseases.

Contrast threshold

In the 1940s, a large study on how well people can see differences in brightness was conducted by Blackwell. Participants were shown discs of different sizes and brightness levels placed against backgrounds with varying levels of light. They had to point out where they believed the disc was located. After analyzing 90,000 observations from seven participants, researchers determined the brightness threshold for a specific disc size and brightness as the level of contrast where participants detected the disc 50% of the time. The experiment used specific contrast levels, leading to distinct threshold values. Smooth curves were drawn to connect these values, and results were recorded. These findings have been widely used in fields like lighting design and road safety.

A separate study by Knoll and others focused on how people detect tiny light sources. Participants adjusted the brightness of the source until it became visible. Hecht later developed a mathematical formula to describe the relationship between brightness and visibility, with separate calculations for low-light (scotopic) and normal-light (photopic) conditions. This formula was later used by Weaver to predict how well stars can be seen with the naked eye and by Schaefer to model star visibility through telescopes.

Crumey found that Hecht’s formula did not accurately describe visibility in very low light conditions, making it unsuitable for studying star visibility. Instead, Crumey created a more accurate model that works for all light levels, from complete darkness to daylight. This model is based on a principle called Ricco’s law, which describes a consistent relationship between light levels and visibility. Crumey used this model to study how well stars can be seen under different conditions and to examine the effects of light pollution on visibility.