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HSV Channels

Tool to separate 3 properties of colors in an image: the hue H, the saturation S and value V (brightness) also called (HSV channels).

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HSV Channels -

Tag(s) : Image Processing

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HSV Channels

Hue/Saturation/Value Channels Separation

What are HSV channels of an image? (Definition)

The HSV (or HSL) color space is a color representation model based on a (human) perception of color.

The hue is commonly called color (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 (over 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.

How to calculate HSV values of an image?

From RGB values (Red, Green, Blue) expressed between 0 and 1.

The brightness $V$ is expressed between 0 and 1 and is calculated $$V = \operatorname{max}(R, G, B)$$

Then calculate the intermediate value $C = V - \operatorname{min}(R, G, B)$

The hue H is expressed in degrees (between 0 and 360) using the formula:

$$H = 60 ^\circ \times \begin{cases} \text{undefined} & \text{if } C = 0 \\ \left( \frac{G - B}{C} \right) \pmod 6 & \text{if } V = R \\ \left( \frac{B - R}{C} + 2 \right) \pmod 6 & \text{if } V = G \\ \left( \frac{R - G}{C} + 4 \right) \pmod 6 & \text{if } V = B \end{cases}$$

The saturation S is expressed between 0 and 1: $$S = \begin{cases} 0 & \text{if } V = 0 \\ \frac{C}{V}, & \text{otherwise} \end{cases}$$

How to separate HSV channels of an image?

For each pixel in an image, calculate the value of T (hue), S (saturation), and L (brightness) and form three grayscale images with them.

dCode performs this separation automatically from an image file.

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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