Understand the purpose of and need for data compression

Why do we compress data? 🤔

Imagine you have a huge suitcase full of clothes. If you could fold them tightly, you could fit more into the same space, or even fit them into a smaller suitcase. Compression does the same thing for digital data:

• Save storage space – fewer bytes = cheaper hard drives or cloud storage.
• Reduce bandwidth usage – faster downloads and uploads.
• Speed up processing – less data to read/write means quicker programs.
• Lower energy consumption – less I/O means less power.

In the real world, this means you can store more photos on a phone, stream videos faster, and keep your laptop lighter.

There are two main families of compression algorithms:

1. Lossless – you can recover the original data exactly. Think of a zipper that can be opened and closed without losing any teeth.
2. Lossy – some data is discarded permanently. Like a photo editor that removes a few pixels to make the file smaller.

Common lossless examples: ZIP, GZIP, PNG. Common lossy examples: JPEG, MP3, MPEG‑4.

Think of a text file as a sentence written in a language that uses many repeated words. If you replace each repeated word with a short code, the sentence becomes shorter. That’s the idea behind dictionary‑based compression (e.g., LZW used in GIF).

Mathematically, if we let n be the number of bits before compression and m after, the compression ratio is:

$ \text{Compression Ratio} = \frac{m}{n} $

Values < 1 mean the file got smaller.

• Define compression and explain why it is useful.
• Distinguish lossless vs lossy and give one example of each.
• Show how to calculate a compression ratio with a small numeric example.
• Use an analogy (e.g., packing clothes) to illustrate the concept.
• Remember that lossy compression is acceptable when quality loss is imperceptible (e.g., JPEG images).
• Be ready to sketch a simple diagram of a compression/decompression cycle.