MM_BSC_SOLNS_2009

MM_BSC_SOLNS_2009 - CM0340 SOLNS CARDIFF UNIVERSITY...

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CM0340 SOLNS CARDIFF UNIVERSITY EXAMINATION PAPER Academic Year: 2008/2009 Examination Period: Autumn Examination Paper Number: CM0340 SOLNS Examination Paper Title: Multimedia SOLUTIONS Duration: 2 hours Do not turn this page over until instructed to do so by the Senior Invigilator. Structure of Examination Paper: There are 14 pages. There are 4 questions in total. There are no appendices. The mark obtainable for a question or part of a question is shown in brackets alongside the question. Students to be provided with: The following items of stationery are to be provided: ONE answer book. Instructions to Students: Answer 3 questions. The use of translation dictionaries between English or Welsh and a foreign language bearing an appropriate departmental stamp is permitted in this examination. 1 PLEASE TURN OVER
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CM0340 SOLNS Q1. (a) Briefly outline the four broad classes of approach that one may exploit to com- press multimedia data. Do not detail any specific compression algorithms . Compression basically employs redundancy in the data: Temporal — in 1D data, 1D signals, Audio etc. correlation between sequential data points. [2] Spatial — correlation between neighbouring pixels or data items in 2D. [2] Spectral — This uses the frequency domain to exploit relationships between frequency of change in data. E.g. in video/imagery, correlation between colour or luminescence components. [2] Psycho-visual — exploit perceptual properties of the human auditory/visual sys- tem. [2] 8 MARKS — BOOKWORK Give one example of a compression algorithm for each class. EXAMPLES: Temporal — Any Audio/Video compression method, Zero length suppression, pattern substitution, Pulse Code Modulation (a few variants), MPEG Audio, MPEG Video, H.264 [1] Spatial — Any Image/Video compression algorithm, GIF, JPEG, MPEG, H.264. [1] Spectral — JPEG, MPEG, H.264. [1] Psycho-visual — MPEG audio, MPEG Video, JPEG (colour conversion). [1] 4 MARKS — BOOKWORK (b) What advantage does arithmetic coding offer over Huffman coding for data com- pression? Huffman coding assumes an integer number (k) of bits for each symbol hence k is never less than 1 [1] Arithmetic coding can represent fractional number of bits and can achieve better compression ratios. [1] 2 MARKS — BOOKWORK (c) Briefly state an algorithm for arithmetic decoding . SEEN In LECTURE Coding: The idea behind arithmetic coding is To have a probability line, 0–1, and Assign to every symbol a range in this line based on its probability, 2
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CM0340 SOLNS Order in terms of probability highest first. Note: The higher the probability, the higher range which assigns to it. For each symbol in the sequence assign a code based in symbols probability and then subdivide for all the symbols: range = high - low; high = low + range * high_range of the symbol being coded; low = low + range * low_range of the symbol being coded; Decoding is the opposite so need to work out ( unseen in lectures ) For current code value: look up in table and assign symbol [1] Eliminate symbol effect by subtracting the low value in the range and divide by range
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MM_BSC_SOLNS_2009 - CM0340 SOLNS CARDIFF UNIVERSITY...

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