Data Compression

Summary: The process of reducing the size of a file by encoding data more efficiently. Can be lossless (no data lost, original fully recoverable) or lossy (some data permanently removed to achieve greater reduction). Essential for efficient storage and transmission. Tags: igcse computer-science Created: 2026-05-08T14:03:00Z Last Updated: 2026-07-16

Why Compress Data?

Data compression reduces the number of bits needed to represent information. The main reasons:

1. Save storage space: Compressed files take up less room on hard drives, SSDs, and other media.
2. Faster transmission: Smaller files transfer more quickly over networks (downloading, streaming, email attachments).
3. Reduce bandwidth costs: Less data sent means lower costs for servers and users (especially important on mobile networks).
4. Fit within limits: Email attachment size limits, message payload limits, and storage quotas often require compression.

Compression is applied at many levels — individual files (ZIP, JPEG, MP3), streaming protocols, and even at the hardware level.

Lossless Compression

Definition: Compression where no data is lost. The original file can be perfectly reconstructed from the compressed version. Used when every bit of data matters.

Run-Length Encoding (RLE)

RLE replaces consecutive identical values with a count and the value. Simple and effective for data with many repeats.

Example: AAAAABBCCCC becomes A5B2C4 (5 As, 2 Bs, 4 Cs).

RLE works well for:

• Simple graphics with large blocks of the same colour (e.g., BMP, icons)
• Fax transmissions
• Data with long runs of identical values

RLE is poor for data with few repeats — it can even increase file size (e.g., ABCDE becomes A1B1C1D1E1).

Huffman Coding

Huffman coding assigns shorter bit patterns to more frequent symbols and longer patterns to less frequent ones. It builds a binary tree where the most common characters are closest to the root (shortest path = fewest bits).

Example: In English text, ‘e’ appears far more often than ‘z’. Huffman coding might assign ‘e’ = 01 (2 bits) and ‘z’ = 11011 (5 bits), reducing the total bit count.

Common Lossless Formats

Lossy Compression

Definition: Compression where some data is permanently removed. The original file cannot be perfectly reconstructed. Used when a close approximation is acceptable and greater size reduction is needed.

Lossy compression works by removing data that is less perceptible to human senses:

• Images (JPEG): Removes fine colour detail the human eye is less sensitive to. High-frequency components in 8x8 pixel blocks are approximated or discarded.
• Audio (MP3): Removes frequencies outside normal hearing range (~20 Hz — 20 kHz), and quieter sounds masked by louder simultaneous sounds (auditory masking).
• Video (MPEG/H.264): Stores only the differences between frames (inter-frame compression) along with spatial compression within each frame.

Trade-offs of Lossy Compression

Common Lossy Formats

Lossless vs Lossy: When to Use Which

Sources

• BBC Bitesize GCSE Computer Science — Data Compression, BBC (free educational resource)
• Cambridge IGCSE Computer Science 0478 — Data Transmission, Cambridge Assessment International Education
• CK-12 Computer Science — Data Compression, CK-12 Foundation (free, CC BY-NC 3.0)

Related Notes

• Lossless Compression — Detailed treatment of RLE, Huffman coding, and LZW
• Lossy Compression — Detailed treatment of JPEG, MP3, and perceptual coding
• File Size Calculation — Calculating compressed vs uncompressed file sizes
• Bitmap Image — Image data before compression
• Sound Sampling — Audio data before compression
• CS-Index

Common Misconceptions