{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Multimedia_BSC_Exam_2010SOLNS (1)

# Multimedia_BSC_Exam_2010SOLNS (1) - CM0340 Solutions...

This preview shows pages 1–4. Sign up to view the full content.

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

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
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
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?

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}