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Sensors are linear devices. If you double the amount of light, the sensor output will double, as long as the pixels are not full (*). Once a pixel reaches full capacity, it will give a constant or "clipped" output. Human vision is non-linear, as explained in the dynamic range topic. A doubling of the light in low light conditions has a much larger effect than in bright conditions. Our vision amplifies the shadows and compresses the highlights. |
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If we expose this sensor until the pixels are full, then the brightest pixels will output a value of 254 (255 would be clipped). If we halve the amount of light, the brightest pixels will output a value of 127. This implies that the brightest stop uses up half of the 255 available tones and this is where human vision is least sensitive. There are only a few tones left to describe the darkest stops, where human vision is more sensitive. This creates a very dark linear RAW image with a histogram skewed to the left. |
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Therefore digital cameras apply a tonal curve to the linear raw data so that images viewed on a monitor or printed images are more pleasing to the eye. Applying a gamma correction of 1/2.2=0.45 will allocate more tones to the shadow areas and fewer tones to the highlight areas in line with the characteristics of our vision. When working in a gamma 2.2 color space like sRGB or Adobe RGB the images will appear perceptually uniform on a monitor or print, avoiding posterization (banding). In reality cameras and raw converters go beyond a gamma correction and apply more of an S-shaped (on a logarithmic scale) curve to the data in order to "compress" the larger dynamic range so it can be represented on a monitor or print in a way that it is pleasing to the human eye. |
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| (*) In practice, there are some non-linearities in the darkest shadows and brightest highlights. Also, some cameras, e.g the Nikon D2X, preprocess the sensor data before the ADC. | ||||
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Article ©1998-2010 Vincent Bockaert and dpreview.com, with permission. | ||||
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Learn : Glossary : Camera System : Sensor Linearity | ||||




