Data Compression Ratio Calculator

Compression ratio is simply the uncompressed size divided by the compressed size, so a 500 MB log file that packs down to 45 MB has a ratio of 11.1 to 1. The complementary figure, space savings, is one minus the inverse of that ratio expressed as a percentage, which for the same file is 91 percent. A third framing, bits per original byte, is useful because it can be compared directly against the theoretical floor set by Shannon entropy: ordinary English prose carries roughly 1 to 1.5 bits of information per character out of the 8 bits used to store it, which is why text routinely compresses by 75 to 90 percent while encrypted or random data cannot be compressed at all and usually grows slightly from container overhead. Expected ratios depend far more on the data than on the algorithm. Plain text, source code, XML and JSON typically reach 3:1 to 10:1; uncompressed bitmaps and WAV audio reach roughly 2:1 to 4:1; JPEG, MP3, MP4 and existing ZIP archives rarely exceed 1.05:1 because their redundancy has already been removed. Algorithm choice mainly moves the speed axis: LZ4 compresses at hundreds of megabytes per second for a modest 1.5:1 to 3:1, DEFLATE and Zstandard sit in the middle, and LZMA trades throughput of a few megabytes per second for ratios of 4:1 to 12:1. Two caveats matter when comparing numbers. Ratios are only meaningful for lossless compression, since lossy codecs change the content, and quoted ratios are corpus-specific, so benchmark figures from the Calgary or Silesia corpora will not predict your own files.

The theoretical foundation was laid by Claude Shannon at Bell Labs in his 1948 paper A Mathematical Theory of Communication, which defined entropy as the average information per symbol and thereby fixed a lower bound on lossless coding. In 1952 David Huffman, then a graduate student at MIT, derived the optimal prefix code for known symbol frequencies as a class assignment, producing the Huffman coding still embedded in ZIP, JPEG and MP3 files. The modern dictionary approach came from Abraham Lempel and Jacob Ziv at the Technion, whose 1977 and 1978 papers introduced the LZ77 sliding-window and LZ78 dictionary methods. Terry Welch refined the latter into LZW in 1984, which was adopted by the Unix compress utility and by the GIF image format. Phil Katz combined LZ77 with Huffman coding into DEFLATE for PKZIP in 1989; Jean-loup Gailly and Mark Adler produced the patent-free gzip in 1992, and DEFLATE was documented as RFC 1951 in 1996. A newer generation arrived once web transfer volumes made small ratio gains valuable: Yann Collet released LZ4 in 2011 for speed, Google published Brotli in 2015 with a built-in dictionary of common web strings, and Collet released Zstandard at Facebook in 2016, pairing LZ77 matching with finite state entropy coding to reach gzip-class ratios at several times the speed.