Huffman Coding assigns binary codes to characters based on their frequencies, ensuring that no code is a prefix of another. This Prefix-Free Property allows efficient decoding, as each code can be uniquely identified without ambiguity. The property is achieved by building a binary tree: • Characters with higher frequencies are closer to the root, resulting in shorter codes. • Leaf nodes represent characters, and the path from the root to a leaf gives its code. The Prefix-Free Property guarantees that decoding proceeds without backtracking, as no code conflicts with another. This is why Huffman Coding is widely used in data compression formats like JPEG and ZIP. Why Other Options Are Incorrect: 1. Binary Search Tree Property: Relates to search trees, not prefix coding. 2. Optimal Substructure: A characteristic of Huffman’s greedy approach but not related to prefix codes. 3. Greedy Choice Property: Explains how Huffman selects frequencies but does not directly enforce prefix-freeness. 4. Overlapping Subproblems: Relates to dynamic programming, not Huffman Coding. The Prefix-Free Property is fundamental to Huffman’s success in lossless data compression.
40% of (362 ÷ 0.05) = ?
The value of ((0.27)2-(0.13)2) / (0.27 + 0.13) is:
3? x 23 ÷ √ 256 = 40.5
[1.45 X 1.45 X 1.45 + 0.55 X 0.55 X 0.55 + 4.785] = ?
5121.3 × 641.8 ÷ 80.5 = 8?
30% of 60% of 1800 + 13 × 14 = (? ÷ 75) × 5
15 × 35 ÷7 + 60% of 300 =?
[(40)2 ÷ 32 × 4] ÷ 50 = ?
23% of 8040+ 42% of 545 = ?%of 3000
12 of 8 - 9 ÷ 3 × 16 - 17 + 6 of 4 - 15