Huffman Coding Visualizer

What is Huffman Coding Visualizer?

Huffman Coding Visualizer builds a Huffman tree from text you supply: it counts symbol frequencies, repeatedly merges the two rarest nodes into a new parent, and stops when a single tree remains. Read the edges from root to leaf and you have each symbol's prefix code. Because you type the input, you can see directly why skewed text compresses well and why uniformly distributed text barely compresses at all — the compression ratio is shown against the fixed-width baseline.

Recommended workflow

1. 1

   More greedy algorithms

   Activity selection shows the same greedy-choice reasoning on intervals.

   Open tool
2. 2

   See 30+ algorithms animated

   DP tables, graphs, string matching and backtracking.

   Open tool

Frequently asked questions

Why do the two rarest symbols get merged first?

Because they end up deepest in the tree, and therefore get the longest codes. Giving the longest codes to the least frequent symbols is what minimises the total encoded length.

Is my tree wrong if it differs from the tool's?

Not necessarily. When frequencies tie, the choice of which node to take is arbitrary, and the resulting trees can differ in shape while producing exactly the same total bit length. Compare totals, not shapes.

What is a prefix code?

A code where no symbol's bit string is a prefix of another's. That property is what lets a decoder read a stream left to right and know exactly where each symbol ends without separators.

Why doesn't my short text compress?

Huffman only pays off when frequencies are skewed and the input is long enough that the savings outweigh storing the code table. Uniform frequencies give you roughly fixed-width codes and no gain.