Maggie HTTN
Visual Algorithms • UX/UI Case Study

Huffman Coding

A greedy algorithm that builds an optimal prefix code for compression by repeatedly merging the two least frequent symbols.

Huffman Coding is a classic algorithm used in file compression. It assigns shorter bit codes to frequent characters and longer codes to rare characters while ensuring the code is prefix-free (no code is the prefix of another).

This visualization shows the merge process step-by-step, then reads off the final binary codes from the tree.

Watch Demo

Quick Facts

Input
Symbols with frequencies (or probabilities).
Output
A prefix-free binary code (and its Huffman tree).
Key Idea
Always merge the two least frequent nodes (greedy choice).
Why it matters
Foundation of real compression formats and encoding pipelines.

Beginner-Friendly Rules

  1. Start with one node per symbol (each labeled with its frequency).
  2. Pick the two smallest frequencies.
  3. Merge them into a parent node whose frequency is the sum.
  4. Put the new node back into the set and repeat until one node remains (the root).
  5. Assign bits along edges: typically left = 0, right = 1.
  6. The code for a symbol is the path from root to its leaf.

What the Visualization Emphasizes

  • Greedy choice clarity: the two minimum nodes are highlighted every step.
  • Stable mental model: “merge → sum → reinsert” repeats with the same visual pattern.
  • Prefix-free meaning: codes end only at leaves, so no code can be the start of another.
  • From tree to bits: the final tree is translated into explicit codes learners can read.

Demo / Media

Animation

This demo shows Huffman Coding step-by-step: selecting the two lowest-frequency nodes, merging them into a parent, repeating until the tree is complete, then reading off the final prefix codes from the root-to-leaf paths.

Common Confusions (and how the UI helps)

“Why two smallest?”
The animation reinforces the greedy rule visually and repeats it consistently so learners trust the process.
“How do I get the codes?”
The final tree is paired with a simple readout: root-to-leaf path becomes a bitstring.
“Is left always 0?”
The choice is conventional, but the important property is prefix-free structure and consistent labeling.
“Do ties matter?”
Ties can produce different (but still optimal) Huffman trees. The UI can show one valid choice clearly.

Outcome

Learners can explain the merge steps, understand why the code is prefix-free, and confidently read the final Huffman codes from the completed tree — which prepares them to implement the algorithm in code.