To display a digital color image, three ideas work together like a well‑rehearsed team: sampling (where to measure), RGB pixels (what color is there), and binary sequences (how the computer stores it). Together, they turn a real‑world scene or an abstract design into millions of tiny colored squares on your screen.

Sampling: Turning a scene into a grid

Imagine looking at a painting through a tiny square window that you slide across in a grid pattern. Each stop is a sample.

  • Sampling is the process of chopping a continuous image (like a photo) into a grid of tiny points called pixels.
  • Each pixel corresponds to a specific position (x,y)(x,y)(x,y) in this grid and represents the color of a very small area of the original scene.
  • The sampling rate (how many pixels per inch or per unit) sets the image resolution: more samples → more pixels → sharper, more detailed image.
  • For example, a 1920×1080 image means the scene was sampled into 1920 columns and 1080 rows, giving over 2 million pixels.

This step answers the “where ” question: Where in the image is this piece of information?

RGB pixels: Mixing colors from three channels

Once we know where the samples are, we need to know what color each one should be.

  • Each pixel’s color is represented using RGB : Red, Green, and Blue components.
  • Think of every pixel as having three tiny “light sliders”: one for red, one for green, one for blue.
  • By adjusting how much red, green, and blue light a pixel emits, we can create millions of colors through additive color mixing (all three high → near white; all three low → black).
  • In a common format, each color channel is stored as a number from 0 to 255:
    • 0 = no light for that color
    • 255 = maximum brightness for that color

Example pixel colors (using RGB values):

  • Pure red: (255, 0, 0)
  • Pure green: (0, 255, 0)
  • Pure blue: (0, 0, 255)
  • White: (255, 255, 255)
  • Black: (0, 0, 0)
  • Gray: (128, 128, 128)

This step answers the “what color ” question: What color should this pixel appear?

Binary sequences: How the computer actually stores color

Computers don’t store “red = 200” as plain text; they store everything as binary —strings of 0s and 1s.

  • Each RGB channel value (0–255) is typically represented using 8 bits of binary.
  • That means:
    • Red uses 8 bits
    • Green uses 8 bits
    • Blue uses 8 bits
  • Together, one pixel uses 24 bits (8+8+8), often called 24‑bit color.

Example:

  • Suppose a pixel’s RGB is (200, 100, 50).
  • In binary (one possible representation):
    • 200 → 11001000
    • 100 → 01100100
    • 50 → 00110010
  • The pixel’s color is stored as a 24‑bit sequence:
    • 11001000 01100100 00110010

For an entire image, the computer keeps a long binary stream that is basically:

Pixel 1’s 24 bits, Pixel 2’s 24 bits, Pixel 3’s 24 bits, … for all pixels in the sampling grid.

This step answers the “how stored ” and “how processed ” questions: How does the machine remember and manipulate these colors?

How they work together, step by step

Bringing it all together, here’s the pipeline from scene to screen:

  1. Sampling the image
    • A camera sensor or graphics program divides the image area into a grid of pixels.
 * Each pixel corresponds to a sampling point that measures the light (or assigned color) at that location.
  1. Assigning RGB values
    • At each pixel position, the system determines how much red, green, and blue are present.
      • In a camera, this comes from light‑sensitive circuits or filtered sensors that measure different color components.
   * In digital art, software sets RGB values directly when you choose colors.
 * These color intensities are represented as three numbers (often 0–255 for each channel).
  1. Encoding in binary sequences
    • Each of the three numbers (R, G, B) is converted into an 8‑bit binary sequence.
 * The three sequences are concatenated into one 24‑bit binary code per pixel.
 * All pixels’ binary codes are stored in memory or on disk, often in image file formats like PNG or JPEG (with extra compression and metadata layered on top).
  1. Rendering on a display
    • When the image is displayed, the computer reads the binary data back.
    • It splits the stream into 24‑bit chunks, decodes each to three 8‑bit values, and sends those as brightness instructions for the red, green, and blue sub‑pixels on your screen.
 * The display hardware aligns these sub‑pixels so that at each pixel position, the three colored lights blend into a single perceived color at normal viewing distance.

So, in one sentence:

Sampling defines which pixel positions exist, RGB values define what color each pixel should display, and binary sequences encode those colors so the computer can store, process, and finally light them up on your screen.

A quick story‑style example

Picture you’re taking a sunset photo on your phone:

  • The sensor samples the scene in a grid: millions of tiny points across the sky, ocean, and horizon.
  • For a pixel over the sun, the sensor may read “a lot of red, some green, a little blue,” which could become RGB (240, 150, 60).
  • Your phone converts those three numbers into 24 bits of binary, then does this for every pixel in the frame.
  • Later, when you open the photo, the device reads those binary sequences again, decodes each pixel’s RGB, and the screen’s red, green, and blue sub‑pixels glow at just the right intensities to recreate that bright orange sunset spot by spot.

From your point of view, you just see a smooth, colorful image—but beneath that, it’s all sampling points, RGB values, and binary bits working together in perfect sync.

TL;DR:
Sampling breaks the image into a pixel grid (position and detail), RGB gives each pixel a specific color using red, green, and blue intensities, and binary sequences encode those RGB values in 0s and 1s so computers can store and display the image accurately on digital screens.

Information gathered from public forums or data available on the internet and portrayed here.