The original data can be perfectly reconstructed (e.g., ZIP files, Huffman coding).
Information theory provides the basis for assessing the strength of encryption (e.g., assessing the entropy of keys).
The goal is to ensure reliable transmission by adding that allows the receiver to detect and correct errors caused by noise. Information and Coding Theory
Shannon proved that if the transmission rate ( ) is less than the capacity ( ), error-free communication is possible. 3. Branches of Coding Theory
): The maximum rate at which information can be reliably transmitted over a noisy channel. The original data can be perfectly reconstructed (e
Hamming codes, Reed-Solomon codes (used in CDs), and modern Low-Density Parity-Check (LDPC) or Polar codes (used in 5G). 4. Summary of Key Differences Source Coding Channel Coding Primary Goal Efficiency (Compression) Reliability (Error Correction) Action Removes redundancy Adds redundancy Metric Channel Capacity ( Timing Performed before transmission Performed during/for transmission 5. Modern Applications
Coding theory is generally divided into two main categories based on the problem they solve: Shannon proved that if the transmission rate (
Coding theory is currently being applied to DNA storage and Quantum error correction to protect information in next-generation systems. ✅ Result