Tool to separate hue, saturation and value/brightness in an image. The separation of the HSV channels (for the 3 properties: hue, saturation and value) is used to analyze the components of the colors of each portion of an image.
HSV Channels - dCode
Tag(s) : Image processing
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HSV Channels
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Hue/Saturation/Value Channels Separation
Tool to separate hue, saturation and value/brightness in an image. The separation of the HSV channels (for the 3 properties: hue, saturation and value) is used to analyze the components of the colors of each portion of an image.
Answers to Questions
How to separate HSV values of an image?
The HSV color space is based on a perceptual representation of color.
The hue is called color in general (mainly red, yellow, green, cyan, blue or magenta). It is common to represent the hue in a circle and give the value of the hue in degrees (360 degrees).
Example: 0° or 360° for red, 60° : yellow, 120° : green, 180° : cyan, 240° : blue, 300° : magenta.
Saturation refers to the intensity of the color between gray (low saturation or desaturation) and pure color (high saturation). The saturation is usually expressed as a percentage or between 0 and 1.
The value corresponds to the brightness of the color, between black (low value) and average saturation (maximum value). The value is usually expressed as a percentage or between 0 and 1.
Why using HSV rather than RGB ?
The main advantage of TSL over RGB is that the luminosity (the intensity of the image / luma), the hue and the saturation (the information on the color / chroma) are separated.
Example: To identify the basic color of a pixel, look at its hue (which is the only correct term for red, yellow, etc.)
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