Show understanding of the impact of changing the sampling rate and resolution

Sampling Rate (Audio)

Think of a recording as a series of snapshots taken of a moving car. The sampling rate $f_s$ is how many snapshots you take per second.

• Higher $f_s$ → more snapshots → smoother, clearer sound.
• Lower $f_s$ → fewer snapshots → gaps, aliasing (like a jumpy video).

🎧 Example: CD audio uses $f_s = 44.1\,\text{kHz}$, which is enough to capture frequencies up to about $22\,\text{kHz}$ (Nyquist theorem: $f_{\text{max}} = \tfrac{f_s}{2}$).

Resolution (Audio & Video)

Resolution is like the number of pixels on a phone screen. More pixels → sharper image, but more data to store.

1. Audio resolution: bits per sample ($N$).
   • 8‑bit audio → 256 possible amplitude levels.
   • 16‑bit audio → 65,536 levels → much quieter noise.
2. Video resolution: pixels per frame.
   • 720p: 1280 × 720 pixels.
   • 1080p: 1920 × 1080 pixels.
   • 4K: 3840 × 2160 pixels.

📷 Analogy: Think of a photo made of tiny dots. If you use a low‑resolution camera, the dots are large and you can’t see fine details. A high‑resolution camera uses tiny dots, giving a clearer picture but needing more storage.

Key Takeaways

• Sampling rate determines how often you capture data points; higher rates reduce aliasing.
• Resolution controls the amount of detail; higher resolution gives clearer audio/video but uses more resources.
• Both parameters must balance quality with storage and transmission limits.

💡 Remember: In exams, you’ll often need to explain the trade‑offs and use formulas like $f_{\text{max}} = \tfrac{f_s}{2}$ or the bit depth equation $N = \log_2(\text{levels})$.