Color models in computer graphics are mathematical representations used to define and display

colors. They provide a way to represent and manipulate colors digitally. Here are three commonly
used color models:

1. RGB (Red, Green, Blue): RGB is an additive color model where colors are created by combining
varying intensities of red, green, and blue light. Each color channel is represented by an 8-bit value
ranging from 0 to 255. For example, (255, 0, 0) represents pure red, (0, 255, 0) represents pure
green, and (0, 0, 255) represents pure blue. By adjusting the intensity of each channel, a wide range
of colors can be achieved. RGB is widely used in computer displays and digital imaging.

2. CMYK (Cyan, Magenta, Yellow, Key): CMYK is a subtractive color model used primarily in printing.
Instead of adding light like RGB, CMYK works by subtracting light from white. Cyan, magenta, and
yellow are the primary colors, and "Key" refers to the key plate, which is typically black. In this
model, colors are created by combining varying amounts of cyan, magenta, yellow, and black inks.
CMYK is used in printing because it can represent a broader range of colors suitable for printed
materials.

3. HSL/HSV (Hue, Saturation, Lightness/Value): HSL and HSV are alternative color models that
describe colors based on their perceptual attributes. They are often used for color selection and
manipulation in graphics software. The H (Hue) component represents the color itself, such as red,
blue, or green. The S (Saturation) component represents the intensity or purity of the color, with 0
being grayscale and 100 being fully saturated. The L (Lightness) or V (Value) component represents
the brightness of the color, with 0 being black and 100 being white. These models provide intuitive
ways to adjust colors based on their visual appearance.

For example, if you want to create a vibrant purple color in RGB, you could set the values to (128, 0,
128). In CMYK, the equivalent color might be (50, 100, 0, 0), indicating a mixture of 50% magenta
and 100% cyan. In HSL or HSV, the hue value would be around 300 degrees (purple range),
saturation would be high, and lightness or value would depend on the desired brightness.

Certainly! Here's a simplified explanation of color models in computer graphics:

1. RGB: Think of RGB as mixing lights. You can create colors by combining red, green, and blue lights.
Imagine three sliders, one for each color. By adjusting the intensity of each slider, you can create a
wide range of colors. For example, to get purple, you increase the red and blue sliders while keeping
the green slider low.

2. CMYK: CMYK is used in printing. Instead of mixing lights, you mix inks. CMYK stands for cyan,
magenta, yellow, and black. By combining different amounts of these inks, you create colors. For
example, to get purple, you would mix magenta and cyan inks.
3. HSL/HSV: HSL and HSV describe colors based on their visual appearance. H stands for hue, which
represents the color itself (e.g., red, blue). S stands for saturation, indicating the intensity or purity
of the color. L (lightness) or V (value) represents the brightness. By adjusting these values, you can
control the color's look and feel.

So, to summarize, RGB is like mixing lights, CMYK is like mixing inks for printing, and HSL/HSV is a
way to describe colors based on how they appear visually.

Gouraud shading is a technique used in computer graphics to make 3D objects look smooth and
realistic. It works by calculating colors at specific points on the object and then blending those colors
smoothly across its surface. This creates a gradual transition of colors, giving the object a polished
appearance without any sudden changes in color.

Advantages of Gouraud shading in easy points:

1. Smooth and polished appearance.

2. Realistic lighting effects like gradients, highlights, and shadows.

3. Efficient for real-time applications like video games.

Disadvantages of Gouraud shading in easy points:

1. Lack of fine surface details.

2. Blurring of sharp edges.

3. Inability to handle specular reflections accurately.

Phong shading is a technique used in computer graphics to make 3D objects appear more realistic
and visually appealing. It calculates the color intensity at each pixel on an object's surface by
interpolating the surface normals (directions) across the polygons. This shading technique takes into
account the angle between the surface normal and the direction of the light source, creating smooth
transitions of lighting and highlights on the object. Phong shading provides a more accurate
representation of how light interacts with surfaces, resulting in improved visual realism.

Here are the key differences between Gouraud shading and Phong shading in easy points:

Gouraud Shading:

1. Colors calculated at vertices: Gouraud shading calculates colors only at the vertices of polygons.

2. Interpolation of colors: It then interpolates these colors across the polygon's surface to create a
smooth shading effect.

3. Faster computation: Gouraud shading is computationally efficient compared to Phong shading.

4. Less accurate specular highlights: It doesn't handle specular highlights, the bright spots caused by
direct light reflection, as accurately as Phong shading.

Phong Shading:

1. Colors calculated at each pixel: Phong shading calculates colors at each pixel on the surface of an
object.

2. Accurate lighting and highlights: It takes into account the angle between the surface normal and
the light source direction, resulting in more accurate lighting and highlights.

3. Slower computation: Phong shading requires more computations compared to Gouraud shading,
making it slower.

4. Handles specular highlights: It can accurately handle specular highlights, making shiny or reflective
surfaces appear more realistic.

In summary, Gouraud shading calculates colors at vertices and interpolates them, providing a
smoother but less accurate representation of lighting. Phong shading calculates colors at each pixel,
resulting in more accurate lighting and handling of specular highlights, albeit at the cost of increased
computational complexity.