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Multimedia_BSC_Exam_2010SOLNS (1)

Multimedia_BSC_Exam_2010SOLNS (1) - CM0340 Solutions...

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CM0340 Solutions CARDIFF UNIVERSITY EXAMINATION PAPER Academic Year: 2009/2010 Examination Period: Autumn Examination Paper Number: CM0340 Solutions Examination Paper Title: Multimedia Duration: 2 hours Do not turn this page over until instructed to do so by the Senior Invigilator. Structure of Examination Paper: There are 13 pages. There are 4 questions in total. There are no appendices. The maximum mark for the examination paper is 80 and 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 Solutions Q1. (a) What is the distinction between lossy and lossless data compression? Lossless Compression — after decompression gives an exact copy of the origi- nal data [1] Lossy Compression — after decompression gives ideally a ‘close’ approxima- tion of the original data, in many cases perceptually lossless but a byte-by- byte comparison of files shows differences. [1] 2 Marks — Bookwork Give one example of a lossy and lossless compression algorithm. Lossless Compression Examples: 1 from Entropy Encoding Schemes (Shannon- Fano Huffman coding), arithmetic coding, LZW algorithm [1] used in GIF image file format. [1] Lossy Compression Examples : 1 from Transform Coding (FFT/DCT based quantisation), differential encoding, vector quantisation [1] 2 Marks — Bookwork (b) List three pattern substitution based compression algorithms. Repetitive Sequence Suppression [1] Run-length Encoding [1] Pattern Substitution [1] 3 Marks — Bookwork For each algorithm, give one application where the method is used with respect to multimedia data . Repetitive Sequence Suppression Example: 1 from Silence suppression in au- dio, ‘white space’ in text, simple uniform backgrounds in images [1] Run-length Encoding : 1 from Computer graphics generated images, Faxes, part of JPEG (latter stage) pipeline [1] Pattern Substitution : 1 from Pattern recognition/token substitution, Entropy coding (Huffman), LZW/GIF, vector quantisation [1] 3 Marks — Bookwork (c) What is the basic concept used in defining an Information Theoretic approach to data compression ? The entropy of an information source S , defined as: H ( S ) = η = i p i log 2 1 p i , is the basis Information Theoretic compression algorithms. [2] 2 Marks — Bookwork 2
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CM0340 Solutions (d) Why is the Huffman coding algorithm better at data compression that the Shannon- Fano Algorithm? (A bottom-up approach) ‘Captures’ the ideal entropy more closely that Shannon-Fano [2] 2 Marks — Bookwork (e) What advantages does the arithmetic coding algorithm offer over Huffman cod- ing algorithm with respect to data compression?
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