Calculating Bits Required for Huffman Coding (AQA GCSE Computer Science): Flashcards
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Huffman coding principle for frequent characters
Huffman coding principle for frequent characters
Get shorter binary codes
Formula: bits for one character type in Huffman
Formula: bits for one character type in Huffman
Frequency × code length
Formula: total bits in Huffman encoded message
Formula: total bits in Huffman encoded message
Bits per character in ASCII encoding
Bits per character in ASCII encoding
7 bits
Type of code lengths in Huffman coding
Type of code lengths in Huffman coding
Variable lengths based on frequency
Type of code lengths in fixed-length encoding
Type of code lengths in fixed-length encoding
Same number of bits for each character
Why Huffman coding saves space
Why Huffman coding saves space
Frequent characters use fewer bits; rare characters use more but don't appear often
Bits for 'HOT HOT HOTTER' (Huffman example)
Bits for 'HOT HOT HOTTER' (Huffman example)
34 bits
Huffman vs ASCII space saving (% reduction in example)
Huffman vs ASCII space saving (% reduction in example)
69% reduction
How to decode binary with Huffman table
How to decode binary with Huffman table
Split sequence using Huffman codes to match characters
Why longer codes for rare characters are optimal
Why longer codes for rare characters are optimal
They appear infrequently, so don't impact overall total much