Data compression uses algorithms to represent data in a smaller, more efficient form — useful for storage and faster transmission over networks. Some compression looks for patterns and repetition in data; run-length encoding, for example, represents repeated pixels in an image (like a long stretch of identical colour) compactly rather than storing each identical pixel separately. Compression comes in two broad types: lossless compression preserves all original data exactly (important for documents or code, where every detail matters), while lossy compression discards some less-noticeable data to achieve smaller file sizes (common for photos, video and audio, where a small quality reduction is often unnoticeable but the file-size savings are significant).
Example
An image with a large solid-blue sky could use run-length encoding to store "500 blue pixels in a row" as a compact instruction instead of storing the colour value of all 500 individual pixels separately — dramatically reducing file size for images with large areas of a single repeated colour or pattern.
Key terms
Lossless compression:
Compression that preserves all original data exactly, with no information lost.
Lossy compression:
Compression that discards some data to achieve a smaller file size.
Questions
1. Data compression aims to represent data:
In a smaller, more efficient form
In an always larger form
With no change to file size
Only for text files
2. Run-length encoding is useful for compressing:
Repeated data, like identical pixels in a row
Only completely random, non-repeating data
Only audio files
Nothing related to images at all
3. Lossless compression:
Preserves all original data exactly
Always deletes random data permanently
Only works on video files
Cannot be reversed at all
4. Lossy compression:
Discards some data to reduce file size
Always preserves every detail perfectly
Only applies to text documents
Increases file size significantly
5. Lossy compression is commonly used for:
Photos, video and audio
Only important legal documents
Only source code files
Only spreadsheets
6. Compression is useful mainly for:
Storage and faster network transmission
Making files impossible to open
Deleting files permanently
Slowing down file transfers
7. A codec is used to:
Compress and decompress audiovisual data
Delete files permanently
Encrypt passwords only
Design webpage layouts
8. Why might lossless compression be essential for compressing a document or a piece of code, but not always necessary for a photo?
Losing even small details in a document or code could change its meaning or function, unlike a photo where minor quality loss often isn't noticeable
Documents and code never need to be compressed at all
Photos always require perfectly lossless compression with zero exceptions
There is no meaningful difference between compressing documents and compressing photos
9. Why does an image with large areas of a single repeated colour compress more effectively using run-length encoding than an image with constantly varying colours?
Run-length encoding is efficient at representing repeated patterns, but offers little benefit when there is little repetition to compress
Run-length encoding works identically well regardless of how much repetition exists in the data
Images with varying colours always compress better than images with repeated colours
Compression algorithms have no relationship to the patterns present in the data
10. Why might streaming services use lossy compression for video, accepting some quality reduction, rather than always streaming uncompressed video?
Uncompressed video files are extremely large, so lossy compression allows much faster streaming with an acceptable quality trade-off
Uncompressed video always streams just as quickly as compressed video
Streaming services never actually use any form of compression
Quality reduction from lossy compression is always highly noticeable to viewers
11. Why is it important that word processing files typically use lossless compression rather than lossy compression?
Losing even small amounts of text or formatting data could corrupt the meaning or usability of the document
Word processing files are never actually compressed in any way
Lossy compression has no effect on the readability of a text document
Documents always benefit from losing some of their data to save space
12. Why might compressing a file that is already highly compressed (like a JPEG image) generally achieve very little further size reduction?
Most of the easily compressible patterns and redundancy have already been removed during the first compression pass
A file can always be compressed by the same amount no matter how many times it has already been compressed
Already-compressed files always compress further by exactly the same amount as uncompressed files
Compression has no relationship to how much redundant or repeated data exists in a file
13. Why might a music streaming app offer both a "data saver" (more compressed) and a "high quality" (less compressed) audio setting?
It lets users choose their own trade-off between file size and data usage versus audio quality, depending on their needs
Offering multiple quality settings has no real benefit to users in any circumstance
Audio quality and file size are always completely unrelated to each other
A single fixed compression setting would always suit every user equally well
14. Why might a compression algorithm identify and encode "patterns" in data rather than just shrinking each piece of data individually?
Repeated or predictable patterns can be represented far more compactly as a single instruction, rather than storing every repetition separately
Patterns in data have no relevance to how efficiently it can be compressed
Compression algorithms only ever work on data with absolutely no repeated patterns
Individual data shrinking is always more efficient than identifying patterns
15. Why might repeatedly saving a photo using lossy compression (compressing an already-compressed file multiple times) gradually reduce its quality noticeably?
Each round of lossy compression discards additional data, so quality loss can accumulate over multiple compressions
Lossy compression only ever discards data the very first time a file is compressed
Repeated compression has no cumulative effect on a file's quality
Lossy compression always restores any previously discarded data automatically
16. Why might choosing the wrong type of compression (like heavily lossy compression) for medical imaging data be a serious problem?
Discarded detail could remove subtle information that is medically important for diagnosis
Medical images never contain any information relevant to diagnosis
Lossy compression always preserves every medically relevant detail perfectly
The type of compression used has no bearing on the usefulness of medical images
17. Why might network bandwidth limitations make compression especially important for video calls and live streaming, compared to downloading a large file overnight?
Live data must be compressed and transmitted in real time within limited bandwidth, unlike a large download which can take as long as needed
Bandwidth limitations have no relevance to live video or streaming
Real-time transmission never actually requires any data compression
Downloading a file overnight faces exactly the same constraints as a live video call
18. Why might a video call service dynamically adjust its compression level (and video quality) based on a user's current internet connection speed?
It balances maintaining a usable call against the available bandwidth, trading some quality for reliability when the connection is weaker
Compression level should always stay completely fixed no matter what the connection speed is
Internet connection speed has no relevance to how a video call should be compressed
Adjusting compression dynamically always produces worse outcomes than using a single fixed setting
19. Why might archiving important historical documents as scanned images require careful consideration of which compression method to use?
Excessive lossy compression could obscure fine details (like faint handwriting) that may be historically significant
The compression method used for archival scans has no bearing on preserving historical detail
All compression methods preserve exactly the same level of detail regardless of type
Historical documents never contain any detail that could be affected by compression choices
20. Why might understanding both lossy and lossless compression help a person make better decisions about which file format to choose for a specific purpose (like emailing a large photo versus saving original artwork)?
Different purposes have different priorities — smaller file size versus preserving every original detail — so the right compression choice depends on the goal
The choice between lossy and lossless compression never actually affects which format is appropriate for a task
Every file format uses identical compression regardless of its intended purpose
File size and detail preservation are always in perfect agreement with no trade-off involved
21. Understanding data compression and how information is encoded mainly helps you to:
Explain how compression algorithms trade off file size and data preservation for different use cases
Assume all compression methods produce identical, unchanged files
Ignore the difference between lossy and lossless approaches
Treat compression as unrelated to storage or network transmission
Answer key (parent copy)
1. In a smaller, more efficient form
2. Repeated data, like identical pixels in a row
3. Preserves all original data exactly
4. Discards some data to reduce file size
5. Photos, video and audio
6. Storage and faster network transmission
7. Compress and decompress audiovisual data
8. Losing even small details in a document or code could change its meaning or function, unlike a photo where minor quality loss often isn't noticeable
9. Run-length encoding is efficient at representing repeated patterns, but offers little benefit when there is little repetition to compress
10. Uncompressed video files are extremely large, so lossy compression allows much faster streaming with an acceptable quality trade-off
11. Losing even small amounts of text or formatting data could corrupt the meaning or usability of the document
12. Most of the easily compressible patterns and redundancy have already been removed during the first compression pass
13. It lets users choose their own trade-off between file size and data usage versus audio quality, depending on their needs
14. Repeated or predictable patterns can be represented far more compactly as a single instruction, rather than storing every repetition separately
15. Each round of lossy compression discards additional data, so quality loss can accumulate over multiple compressions
16. Discarded detail could remove subtle information that is medically important for diagnosis
17. Live data must be compressed and transmitted in real time within limited bandwidth, unlike a large download which can take as long as needed
18. It balances maintaining a usable call against the available bandwidth, trading some quality for reliability when the connection is weaker
19. Excessive lossy compression could obscure fine details (like faint handwriting) that may be historically significant
20. Different purposes have different priorities — smaller file size versus preserving every original detail — so the right compression choice depends on the goal
21. Explain how compression algorithms trade off file size and data preservation for different use cases