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