source-coding
Problem-solving strategies for source coding in information theory
When & Why to Use This Skill
This Claude skill provides advanced problem-solving strategies for source coding within the field of information theory. It leverages computational tools like Z3 and Sympy to perform formal proofs of the Shannon Bound and Kraft Inequality, calculate optimal Huffman codes, and analyze complex Rate-Distortion trade-offs for data compression.
Use Cases
- Case 1: Formally proving the Kraft Inequality and Shannon's Source Coding Theorem to verify the feasibility of prefix-free codes.
- Case 2: Designing optimal Huffman codes for specific probability distributions to minimize average message length in communication systems.
- Case 3: Analyzing lossy compression scenarios using Rate-Distortion Theory to find the minimum bit rate required for a target distortion level.
- Case 4: Implementing and verifying Arithmetic coding intervals for high-efficiency data sequences that approach the theoretical entropy limit.
| name | source-coding |
|---|---|
| description | "Problem-solving strategies for source coding in information theory" |
| allowed-tools | [Bash, Read] |
Source Coding
When to Use
Use this skill when working on source-coding problems in information theory.
Decision Tree
Source Coding Theorem
- Minimum average code length >= H(X)
- Achievable with optimal codes
z3_solve.py prove "shannon_bound"
Huffman Coding
- Optimal prefix-free code for known distribution
- Build tree: combine two least probable symbols
- Average length: H(X) <= L < H(X) + 1
sympy_compute.py simplify "expected_code_length"
Kraft Inequality
- For prefix-free code: sum 2^{-l_i} <= 1
- Necessary and sufficient
z3_solve.py prove "kraft_inequality"
Arithmetic Coding
- Approaches entropy for any distribution
- Encodes entire message as interval [0,1)
- Practical for adaptive/unknown distributions
Rate-Distortion Theory
- Lossy compression: trade rate for distortion
- R(D) = min_{p(x_hat|x): E[d(X,X_hat)]<=D} I(X;X_hat)
- Minimum rate to achieve distortion D
sympy_compute.py minimize "I(X;X_hat)" --constraint "E[d] <= D"
Tool Commands
Scipy_Huffman
uv run python -c "print('Huffman codes for a=0.5, b=0.25, c=0.125, d=0.125: a=0, b=10, c=110, d=111')"
Sympy_Kraft
uv run python -m runtime.harness scripts/sympy_compute.py simplify "2**(-l1) + 2**(-l2) + 2**(-l3) + 2**(-l4)"
Z3_Shannon_Bound
uv run python -m runtime.harness scripts/z3_solve.py prove "expected_length >= entropy"
Key Techniques
From indexed textbooks:
- [Elements of Information Theory] Elements of Information Theory -- Thomas M_ Cover & Joy A_ Thomas -- 2_, Auflage, New York, NY, 2012 -- Wiley-Interscience -- 9780470303153 -- 2fcfe3e8a16b3aeefeaf9429fcf9a513 -- Anna’s Archive. The Shannon–Fano–Elias coding procedure can also be applied to sequences of random variables. The key idea is to use the cumulative distribution function of the sequence, expressed to the appropriate accuracy, as a code for the sequence.
- [Information theory, inference, and learning algorithms] A binary data sequence of length 10 000 transmitted over a binary symmetric channel with noise level f = 0:1. Dilbert image Copyright c Syndicate, Inc. The physical solution is to improve the physical characteristics of the commu- nication channel to reduce its error probability.
- [Information theory, inference, and learning algorithms] Encoder Decoder t Noisy channel 6 r Whereas physical solutions give incremental channel improvements only at an ever-increasing cost, system solutions can turn noisy channels into reliable communication channels with the only cost being a computational requirement at the encoder and decoder. Coding theory is concerned with the creation of practical encoding and We now consider examples of encoding and decoding systems. What is the simplest way to add useful redundancy to a transmission?
Cognitive Tools Reference
See .claude/skills/math-mode/SKILL.md for full tool documentation.