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Calculadora de Taxa de Compressão

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Calculadora de Taxa de Compressão

Calculate compression ratio, space savings percentage, and size reduction from original and compressed file sizes. Enter sizes in any unit (B, KB, MB, GB, TB) and instantly see the compression ratio, bits per byte, space saved, and effective throughput metrics.

Como usar

Enter the original file size and its unit, then the compressed file size and its unit. All compression metrics update instantly: ratio, space savings percentage, bits saved per input byte, and size difference.

Características

Mixed unit support – original and compressed sizes can use different units
Compression ratio – displayed as X:1 ratio
Space savings % – percentage reduction from original size
Bits per byte metric – useful for evaluating entropy-based compression efficiency
Size difference – absolute bytes saved in human-readable form
Real-time updates – all metrics update as you type

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 Perguntas frequentes

What is compression ratio and how is it calculated?

Compression ratio is the ratio of the original data size to the compressed size: Ratio = Original Size / Compressed Size. A ratio of 3:1 means the compressed file is one-third the size of the original, saving 66.7% of space. Space savings percentage is calculated as (1 − Compressed/Original) × 100. A ratio below 1:1 means the compressed file is larger than the original, which can happen with already-compressed data.
Why do some files compress poorly or become larger after compression?

Files that are already compressed (JPEG, MP4, ZIP, PNG) contain high-entropy data with no repetitive patterns for compressors to exploit, so re-compressing them yields no benefit and may slightly increase size due to compression headers. Truly random data has maximum entropy and is incompressible by definition. Lossless compressors like gzip, zstd, and bzip2 work best on text, logs, CSV, and other repetitive structured data.
What compression algorithms achieve the best ratios?

General-purpose lossless compressors ranked approximately by ratio (best to fastest): bzip2 > zstd (high levels) > gzip > LZ4. For text, bzip2 and LZMA (7-zip) typically achieve 3:1 to 10:1 ratios. For raw log files, zstd at high compression levels can achieve 10:1 to 20:1. Specialised compressors (like those for genomic data or floating-point scientific data) can achieve much higher ratios by exploiting domain-specific patterns.
What is the relationship between compression ratio and entropy?

Shannon entropy measures the theoretical minimum bits needed to represent data, setting the upper bound on lossless compression. Data with entropy of 4 bits/byte can be compressed to at most 50% of its original size. High-entropy data (random bytes, encrypted data, already-compressed files) is near the entropy limit and yields little or no further compression. The incompressibility of encrypted data is actually a quality indicator — it confirms the ciphertext looks random.