Some information is discarded to achieve higher compression, acceptable in multimedia (e.g., JPEG, MP3). B. Channel Coding (Error Control)
Modern image and video compression often integrate classical rate-distortion theory with neural networks.
): The maximum rate at which information can be reliably transmitted over a noisy channel. Information and Coding Theory
H(X)=−∑x∈XP(x)logbP(x)cap H open paren cap X close paren equals negative sum over x is an element of cap X of cap P open paren x close paren log base b of cap P open paren x close paren where the unit is if Channel Capacity (
4G and 5G networks use advanced channel codes to maintain high speeds in noisy environments. Some information is discarded to achieve higher compression,
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
This report provides a comprehensive overview of , foundational disciplines that define how we quantify, compress, and reliably transmit data in the digital age. 1. Introduction and History ): The maximum rate at which information can
is the mathematical study of the quantification, storage, and communication of information. It was founded by Claude Shannon in his landmark 1948 paper, "A Mathematical Theory of Communication" , which introduced a probabilistic framework to address the fundamental limits of communication.