Compression
| English | Chinese | Pinyin |
|---|---|---|
| compression | 压缩 | yā suō |
| bandwidth | 带宽 | dài kuān |
| lossless | 无损 | wú sǔn |
| lossy | 有损 | yǒu sǔn |
| run-length encoding | 行程编码 | xíng chéng biān mǎ |
| dictionary coding | 字典编码 | zì diǎn biān mǎ |
| Huffman coding | 霍夫曼编码 | huò fū màn biān mǎ |
Making files smaller
- Compression 压缩 reduces a file's size — saving storage and transmission bandwidth 带宽.
- There are two families: lossless 无损 and lossy 有损.
- Choosing the right one depends on whether you can afford to lose any detail.
Lossless vs lossy
- Lossless — the original data is recovered exactly (ZIP, PNG; text, programs).
- Lossy — some detail is thrown away for much smaller files (JPEG, MP3, video).
- Use lossless for documents, source code, medical images — anything needing exact data.
- Use lossy for streaming media. Real-time video must send huge data over limited bandwidth, so it must use lossy — lossless would not shrink it enough and the picture would freeze.
Compression methods: lossless vs lossy, with common examples
Lossless compression means:
Lossless compression lets you rebuild the original data exactly — essential for text, programs and ZIP/PNG.
Which file should be compressed losslessly?
Source code must be recovered exactly — a single changed character could break it — so it needs lossless compression.
Why does real-time video streaming use lossy compression?
Raw HD video is gigabytes per minute; only lossy compression shrinks it enough to stream in real time without freezing.
Match each compression idea to what it means.
Lossless keeps every bit (needed for text/code); lossy trades quality for size (photos, audio).
Lossless compression rebuilds the original data exactly (needed for text and programs), while lossy compression permanently removes some data to shrink the file (used for photos and audio).
That is why a program or a ZIP must be lossless, but a photo or a song can use lossy compression.
Lossless methods
- Run-length encoding 行程编码 (RLE) — store "the next $n$ values are $x$" instead of repeating $x$. Great for flat areas; useless for noisy data.
- Dictionary coding 字典编码 (ZIP, PNG) — replace repeated byte sequences with a short reference. Good for text and code.
- Huffman coding 霍夫曼编码 — give short codes to common symbols and long codes to rare ones, lowering the average length.
Run-length encoding (a lossless method)
Run-length encoding replaces a run of repeated values with one value plus a count. It is lossless — the original rebuilds exactly — but only shrinks data that has long runs.
Run-length encoding works best on data that has:
RLE replaces a run of identical values with a count + value, so it shines on flat areas and is useless on noisy data.
Huffman coding reduces size by:
Huffman assigns the shortest codes to the most frequent symbols, lowering the average code length.
Lossy methods
- Images (JPEG) — drop fine detail and colour differences the eye barely notices.
- Sound (MP3, AAC) — drop pitches we hear poorly, and quiet sounds masked by louder ones.
- Video — combines spatial compression (within each frame, like JPEG) with temporal compression (most frames store only the differences from the previous frame).
- Compression applies to text, bitmap image and vector graphic files alike.
Temporal compression of video works by:
Temporal compression stores how each frame differs from the one before, since most of the picture stays the same between frames. (Spatial compression handles within-frame detail.)
You've got it
- lossless = exact recovery (ZIP/PNG, text, code); lossy = detail dropped (JPEG/MP3/video)
- lossless methods: RLE (runs), dictionary (repeated sequences), Huffman (common = short code)
- real-time video streaming uses lossy — limited bandwidth, huge data
- video = spatial (per frame) + temporal (differences between frames) compression