To understand how computers process images, we need to start at the most basic level. Forget the continuous scene your eyes perceive for a moment. A digital image, from a computer's perspective, isn't a smooth picture but rather a collection of tiny, individual dots of color or intensity. Each of these dots is called a pixel, short for "picture element."

Think of a digital image like a giant mosaic made of tiny, uniformly sized square tiles, or perhaps a grid on graph paper. Each square in this grid represents one pixel. It's the smallest controllable element of a picture represented on the screen or stored in memory.

What Defines a Pixel?

Every pixel in an image has two primary characteristics:

Location: Just like finding a square on graph paper, each pixel has a specific position within the image grid. This position is usually defined by coordinates, typically an (x, y) or (row, column) pair. Conventionally, in many image processing libraries, the coordinate system starts at the top-left corner of the image, with the x-axis pointing right and the y-axis pointing down. So, the pixel at the very top-left corner would be at coordinates (0, 0).
Value: Each pixel also holds a value (or sometimes multiple values) that describes its appearance, specifically its color or brightness.

Imagine zooming way into a digital photograph until you can no longer see distinct objects, but only squares of uniform color. Those squares are the pixels.

A simplified representation of an image as a grid of pixels. Each cell has a location (row, column) and a value determining its appearance.

Pixels and Image Structure

A computer doesn't "see" a cat or a landscape in an image initially. It sees a large, two-dimensional array (a grid or matrix) of numbers. Each number in this grid corresponds to the value of a pixel at a specific location.

For instance, a simple grayscale (black and white) image might be represented as a grid where each pixel has a single value indicating its intensity, typically ranging from 0 (black) to 255 (white).

Consider a tiny 3x3 grayscale image:

Location (row, col)	Pixel Value (Intensity)
(0, 0)	50
(0, 1)	65
(0, 2)	80
(1, 0)	100
(1, 1)	115
(1, 2)	130
(2, 0)	155
(2, 1)	170
(2, 2)	185

To the computer, this image is just a grid of numbers:

\begin{bmatrix} 50 & 65 & 80 \\ 100 & 115 & 130 \\ 155 & 170 & 185 \end{bmatrix}

This numerical, grid-based structure is fundamental. It allows computers to easily store, access, and manipulate image data using mathematical operations. Operations like changing brightness might involve adding a constant value to every pixel, while more complex tasks involve analyzing patterns within these numerical values.

Understanding that images are composed of pixels, each with a location and a value, is the first step towards comprehending how image processing and computer vision algorithms work. We will build upon this concept as we explore how these pixel values represent colors and how we can manipulate them.