Huffman Coding Calculator
Build Huffman codes for text compression. See the encoding table, compressed output, and compression ratio.
Huffman Coding Calculator
How to Use the Huffman Coding Calculator
- Type or paste the text you want to compress into the input field.
- Click Encode — the calculator counts character frequencies and builds the Huffman tree.
- Review the encoding table showing each character's Huffman code and the bits it uses.
- Check the summary for original size, compressed size, compression ratio, and average code length.
Anwendungsfälle
- •Learning how lossless compression algorithms work in computer science courses.
- •Demonstrating the relationship between character frequency and code length.
- •Comparing Huffman compression efficiency across different types of text.
- •Understanding the theoretical basis for real-world compression formats like DEFLATE (used in ZIP and PNG).
Formel
Huffman coding is a greedy prefix-free encoding algorithm. It builds a binary tree where each leaf represents a character weighted by its frequency. More frequent characters receive shorter codes. The algorithm: (1) count character frequencies; (2) build a min-heap of single-node trees; (3) repeatedly merge the two lowest-frequency nodes until one tree remains; (4) assign 0/1 to left/right branches to derive codes.
Häufig gestellte Fragen
What is Huffman coding?
Huffman coding is a lossless data compression algorithm invented by David A. Huffman in 1952. It assigns variable-length binary codes to characters based on their frequency — frequent characters get short codes and rare characters get long ones. The resulting codes are prefix-free, meaning no code is a prefix of another, which allows unambiguous decoding.
How is the compression ratio calculated?
The original size assumes 8 bits per character (standard ASCII/UTF-8 for English text). The compressed size is the sum of (code length × frequency) for all characters. Compression ratio = compressed size / original size. A ratio below 1 (or below 100%) indicates space savings. Typical English text achieves 40–60% of original size.
Why does Huffman coding require at least 2 different characters?
Huffman coding builds a binary tree that assigns distinct bit patterns to each character. If all characters in the input are the same, there is only one leaf node and no branching is possible — the tree cannot produce a valid prefix-free code. In practice, a string of one unique character would be encoded as a single bit repeated, but this edge case requires special handling outside the standard algorithm.