Source Coding and the Entropy Bound
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Each question is graded on a single answer: a check that this chapter's methods work, not a test of the reasoning behind them.
A memoryless source emits a, b, c, d with probabilities 0.5, 0.25, 0.125, 0.125. Compute its entropy in bits per symbol.
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info-source-coding-theorem-9All Information Theory chaptersAbout these 66 questions
Shannon's source coding theorem in both directions, and Kraft's inequality — why entropy is the floor no lossless code can beat. This set covers the chapter Source Coding and the Entropy Bound in Information Theory: 19 easy, 30 medium and 17 hard questions, each graded instantly with a hint and a full step-by-step solution. Progress is saved in this browser only.
- What the Theorem Says — 11 questions
- Achievability: Codes That Reach the Entropy — 11 questions
- The Converse: Why Entropy Cannot Be Beaten — 11 questions
- Lossless and Lossy Compression — 11 questions
- Redundancy, Models, and the Cost of Being Wrong — 11 questions
- Prefix Codes, Kraft's Inequality and the Entropy Bound — 11 questions