The concept of information entropy, as defined by Claude Shannon, relates the probability of a message to its information content, where less probable messages carry more information. This principle is fundamental to data compression, suggesting that more frequent symbols should be assigned shorter codes and less frequent symbols longer codes to minimize average bit length. Higher entropy indicates a more random and less predictable system, making it harder to compress. AI
RANK_REASON Explains a fundamental concept in information theory and data compression.
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