A) David A. Huffman B) Robert Johnson C) John Smith D) Alice Jones
A) Fixed-length encoding B) Variable-length encoding C) Binary encoding D) ASCII encoding
A) Frequent symbols B) Symbols starting with A C) Symbols at odd indices D) Rare symbols
A) A code where no codeword is a prefix of another B) A code that uses only 0s and 1s C) A code with equal-length codewords D) A code that starts with the same symbol
A) Optimal binary tree B) Complete tree C) Perfect tree D) Balanced tree
A) Memory consumption B) Compression ratio C) Number of symbols D) Encoding speed
A) O(n2) B) O(n log n) C) O(log n) D) O(n)
A) Assigning binary codes to symbols B) Calculating symbol frequencies C) Compressing the data D) Building a linked list
A) Symbol with a prime number B) Most frequent symbol C) Least frequent symbol D) Symbol with the longest name
A) Queue B) Linked list C) Binary heap D) Stack
A) Prefix codes B) Postfix codes C) Suffix codes D) Infix codes
A) 1949 B) 1955 C) 1952 D) 1960
A) Priority queue B) Stack C) Array D) Queue
A) Audio file compression. B) Fax machines. C) Text compression in word processors. D) Image encoding for web pages.
A) MIT B) Harvard University C) Princeton University D) Stanford University
A) They are removed from the tree B) They are combined into a new internal node C) They become root nodes D) They remain as leaf nodes
A) The second queue B) Neither queue C) Both queues simultaneously D) The first queue
A) H(A) = ∑(w_i > 0) log2(w_i) B) H(A) = ∑(w_i > 0) h(a_i) / w_i C) H(A) = ∑(w_i > 0) w_i / log2(w_i) D) H(A) = -∑(w_i > 0) w_i * log2(w_i)
A) Randomly select an item from either queue B) Choose the item in the first queue C) Choose the item in the second queue D) Remove both items and start over
A) h(a_i) = log2(1 / w_i) B) h(a_i) = w_i * log2(w_i) C) h(a_i) = -log2(w_i) D) h(a_i) = 2w_i
A) Minimizing the maximum weighted path length, among others. B) Problems that do not involve weights. C) Only compression-related problems. D) Problems related to sorting data.
A) Lempel-Ziv-Welch (LZW) B) Shannon-Fano coding C) Run-length encoding D) Arithmetic coding
A) An internal node B) Following the right child C) A leaf node D) Following the left child
A) T. C. Hu. B) Alan Turing. C) Richard M. Karp. D) Adriano Garsia.
A) An encryption key must accompany the compressed data. B) The original text must be stored alongside the compressed version. C) No additional information needs to be stored. D) A frequency table must be stored with the compressed text.
A) By sorting both queues by weight after each insertion B) By only enqueuing nodes with unique weights C) By keeping initial weights in the first queue and combined weights in the second queue D) By randomly selecting nodes from either queue
A) Template Huffman algorithm. B) Adaptive Huffman algorithm. C) Binary Huffman algorithm. D) The package-merge algorithm.
A) It equals the inverse of its weight B) It contributes negatively to the entropy C) It is equal to the symbol's information content D) Zero, since lim_(w→0+) w * log2(w) = 0
A) Two B) Three C) Four D) One
A) The frequency of occurrence. B) The binary representation. C) The transmission cost. D) The alphabetic order. |