We can see when the image has low brightnesses and low contrast, we can process it by Gamma Correction, and the value of gamma should be less than 1.Because the algorithm can expand low gray steps and compress the high gray steps when γ < 1. Over correction (in addition to making mid-tones too light) shifts colors towards neutral grey, while under correction (in addition to making mid-tones too dark) shifts colors towards the display primaries. If an image is under or over gamma corrected, this also affects the color balance. Otherwise, an excess of bits would be devoted to describing the brighter tones (where the camera is relatively more sensitive), and a shortage of bits would be left to describe the darker tones (where the camera is relatively less sensitive):
Since gamma encoding redistributes tonal levels closer to how our eyes perceive them, fewer bits are needed to describe a given tonal range. Gamma encoded images store tones more efficiently. A gamma value γ 1 is called a decoding gamma and the application of the expansive power-law nonlinearity is called gamma expansion.” Where A is a constant and the input and output values are non-negative real values in the common case of A = 1, inputs and outputs are typically in the range 0–1. “Gamma correction is, in the simplest cases, defined by the following power-law expression: Here is the definition of Gamma Correction in Wikipedia:
OPEN BROADCASTER STUDIO GAMMA CONTROL CODE
Gamma Correction is the name of a nonlinear operation used to code and decode luminance or tristimulus values in video or still image systems. CRTs were not able to amplify the input signal themselves and thus the output signal from the PC needed to be adjusted, giving rise to (as of today) standard gamma 2.2 correction and sRGB color space. Gamma correction was originally designed to compensate for CRT monitors’ non-linear response to the input signal.
OPEN BROADCASTER STUDIO GAMMA CONTROL HOW TO
In this tutorial, I will introduce Gamma Correction and show you how to use it with OpenCV. There are many algorithms used for Illumination Compensation such as Histogram equalization, Color similarity measure, Gamma Correction and so on. To make objects recognizable in pictures, we need to process the photo with Illumination Compensation. In reality, we can always see some photos that have low brightnesses and low contrast.
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