Why does Huffman coding, applied to a source with highly skewed symbol probabilities, approach its theoretical compression limit?

💡 Explanation

Huffman coding achieves optimal compression when codeword lengths approximate the Shannon information content of each symbol because this minimizes average code length. The mechanism is information entropy. Therefore, skewed probabilities lead to shorter average codeword lengths, rather than overflow errors; optimization and quantization relate to different coding methods.

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