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