Fundamentals of Image Processing

Color Models

Computers store and display images using pixels, which are tiny squares that make up a picture. Each pixel holds color information, and when you put millions of them together, you get a clear image. The way colors are represented depends on different color models:

• Grayscale – Instead of color, this model uses different shades of gray (from 0 to 255), from white to black. It’s often used in black-and-white photography or medical imaging.

• RGB (Red, Green, Blue) – The most common model, used in screens and cameras. It combines different levels (from 0 to 255) of red, green, and blue light to create all possible colors.

• HSV (Hue, Saturation, Value) – A model that represents color in a way that’s closer to how humans see it. It breaks colors down into their type (hue, from 0˚ to 360˚ ), intensity (saturation, from 0% to 100%), and brightness (value, from 0% to 100%).

Vector vs. Raster Images

When working with images, it's important to understand the two main types: raster images and vector images. Each has its own way of storing visual data and is used for different purposes.

Raster Images (Pixel-Based) Raster images are made up of tiny squares called pixels, arranged in a grid. Each pixel has a color value, and together, they form the complete image. Raster images are commonly used in photography and digital displays because they accurately capture details and colors.

• Common Formats: JPEG, PNG, BMP, TIFF

• Pros: rich detail and color depth

• Cons: lose quality when resized (pixelation)

Vector Images (Mathematical Shapes) Vector images, on the other hand, are not made of pixels but mathematical equations defining lines, curves, and shapes. Because of this, they can be resized infinitely without losing quality. These images are ideal for logos, icons, and illustrations.

• Common Formats: SVG, EPS, PDF

• Pros: scalable without losing clarity

• Cons: not suitable for detailed photos

Each format serves a different purpose, whether it’s saving space, preserving quality, or supporting special effects like transparency. Understanding how images are stored and represented helps in choosing the right format for any task.