Multimedia Computing (BSc CSIT, CSC467): the questions likely to come
37 analyzed questions from 8 past papers (2074-2082), grouped by syllabus unit — each with its probability, how often it's been asked, and where to study the answer.
Explain entropy and source coding for multimedia. Discuss Huffman coding, run-length encoding and arithmetic coding, and construct a Huffman code for a given set of symbol frequencies.
Entropy and Source Coding for Multimedia
Entropy measures the average information content of a source. For a source with symbols of probability :
Entropy gives the theoretical lower bound on the average number of bits per symbol for lossless coding (Shannon's source coding theorem). Source (entropy) coding assigns shorter codewords to more probable symbols to approach this bound.
Huffman Coding
A prefix-free, variable-length code built bottom-up: repeatedly merge the two least-probable symbols into a new node until one tree remains; assign 0/1 along branches. It is optimal among integer-length prefix codes. Limitation: each symbol uses a whole number of bits, so it can be up to ~1 bit/symbol away from entropy.
Run-Length Encoding (RLE)
Replaces runs of identical symbols by a (value, count) pair, e.g. AAAAABBB → 5A3B. Very effective for data with long runs (e.g. quantized DCT coefficients, fax images, GIF) but poor for noisy/varied data.
Arithmetic Coding
Encodes the entire message as a single fractional number in [0, 1). It does not require integer bit lengths, so it can come arbitrarily close to entropy and often outperforms Huffman, especially for skewed probabilities. Cost: more computation and patent/complexity issues historically.
Worked Huffman Example
Symbols and frequencies: A=45, B=13, C=12, D=16, E=9, F=5 (total 100).
Merge least-probable repeatedly:
- F(5)+E(9) = 14
- C(12)+B(13) = 25
- (14)+D(16) = 30
- (25)+(30) = 55
- A(45)+(55) = 100
Resulting codes (one valid assignment):
| Symbol | Freq | Code | Length |
|---|---|---|---|
| A | 45 | 0 | 1 |
| C | 12 | 100 | 3 |
| B | 13 | 101 | 3 |
| F | 5 | 1100 | 4 |
| E | 9 | 1101 | 4 |
| D | 16 | 111 | 3 |
Average length bits/symbol, well below the fixed 3 bits needed for 6 symbols.
Multimedia Data Compression
Explain entropy and source coding for multimedia. Discuss Huffman coding, run-length encoding and arithmetic coding, and construct a Huffman code for a given set of symbol frequencies.
Explain the MPEG video compression standard. Discuss I-frames, P-frames and B-frames, motion estimation and compensation, and the group of pictures (GOP) structure.
Explain the JPEG image compression standard. Describe its main steps - DCT, quantization, zig-zag ordering and entropy (Huffman) coding - with the help of a block diagram.
Define entropy and hybrid coding. Explain about lossy sequential DCT based model.
What is Huffman coding? Construct a Huffman code for a given set of symbols and explain its working.
Explain run-length encoding (RLE) with an example.
Explain the role of DCT and quantization in JPEG compression.
Differentiate between I-frames, P-frames and B-frames in MPEG.
Differentiate between lossy and lossless compression with examples.
Define entropy in the context of coding. How is it related to compression?
Explain arithmetic coding and how it differs from Huffman coding.
Explain motion estimation and motion compensation in video compression.
Write short notes on MP3 audio compression and psychoacoustic models.
Explain the Discrete Cosine Transform (DCT) and its importance in image compression.
Why do we need to compress multimedia data? Discuss the roles of user interface in multimedia system.
Sit a probable paper
A full mock exam built from the most likely questions, mirroring the real paper's structure. Every slot is a real past question.
Most Probable Paper
Mirrors the real structure · 60 marks · based on 8 past papers
- 1.[10 marks]
Explain entropy and source coding for multimedia. Discuss Huffman coding, run-length encoding and arithmetic coding, and construct a Huffman code for a given set of symbol frequencies.
This question has recurred in 3 of 8 years; so far only in internal assessments, not the board; and its topic (Multimedia Data Compression) appears in 100% of years.
- 2.[10 marks]
Explain the MPEG video compression standard. Discuss I-frames, P-frames and B-frames, motion estimation and compensation, and the group of pictures (GOP) structure.
This question has recurred in 3 of 8 years; so far only in internal assessments, not the board; and its topic (Multimedia Data Compression) appears in 100% of years.
- 3.[10 marks]
Explain the JPEG image compression standard. Describe its main steps - DCT, quantization, zig-zag ordering and entropy (Huffman) coding - with the help of a block diagram.
This question has recurred in 3 of 8 years; so far only in internal assessments, not the board; and its topic (Multimedia Data Compression) appears in 100% of years.
- 1.[5 marks]
What is Huffman coding? Construct a Huffman code for a given set of symbols and explain its working.
This question has recurred in 4 of 8 years; so far only in internal assessments, not the board; and its topic (Multimedia Data Compression) appears in 100% of years.
- 2.[5 marks]
Explain run-length encoding (RLE) with an example.
This question has recurred in 4 of 8 years; so far only in internal assessments, not the board; and its topic (Multimedia Data Compression) appears in 100% of years.
- 3.[5 marks]
Explain the role of DCT and quantization in JPEG compression.
This question has recurred in 4 of 8 years; so far only in internal assessments, not the board; and its topic (Multimedia Data Compression) appears in 100% of years.
- 4.[5 marks]
Differentiate between I-frames, P-frames and B-frames in MPEG.
This question has recurred in 4 of 8 years; so far only in internal assessments, not the board; and its topic (Multimedia Data Compression) appears in 100% of years.
- 5.[5 marks]
Differentiate between lossy and lossless compression with examples.
This question has recurred in 4 of 8 years; so far only in internal assessments, not the board; and its topic (Multimedia Data Compression) appears in 100% of years.
- 6.[5 marks]
What are the characteristics of multimedia data? Explain the storage requirements.
This question has recurred in 4 of 8 years; so far only in internal assessments, not the board; and its topic (Introduction to Multimedia) appears in 100% of years.
- 7.[5 marks]
Differentiate between the RGB and CMYK color models.
This question has recurred in 4 of 8 years; so far only in internal assessments, not the board; and its topic (Multimedia Authoring and Data Representations) appears in 100% of years.
- 8.[5 marks]
Explain sampling and quantization of digital audio.
This question has recurred in 4 of 8 years; so far only in internal assessments, not the board; and its topic (Fundamental Concepts in Audio and Video) appears in 100% of years.
- 9.[5 marks]
What is multimedia synchronization? Differentiate intra-media and inter-media synchronization.
This question has recurred in 4 of 8 years; so far only in internal assessments, not the board; and its topic (Multimedia Networks and Communication) appears in 88% of years.
Behind the numbers
The raw evidence the predictions are computed from: marks per unit per year, syllabus weights, trends, and coverage.
Show the heatmap, topic table and coverage analysis
The receipt: marks per unit, per year
Each row is a syllabus unit, each column an exam year, each cell the marks that unit earned that year. Click any cell to see the actual questions behind it.
| # | Syllabus unit | Probability | Appeared | Avg marks | Syllabus weight | Exam vs syllabus | Trend | Questions |
|---|---|---|---|---|---|---|---|---|
| 1 | U4Multimedia Data Compression | Very likely100% | 35 | 31%14 lecture hrs | Over-examinedexam 47% · syllabus 31% | Fading | 13 recurring15 total | |
| 2 | U1Introduction to Multimedia | Very likely100% | 13.1 | 11%5 lecture hrs | Over-examinedexam 18% · syllabus 11% | Steady | 4 recurring9 total | |
| 3 | U2Multimedia Authoring and Data Representations | Very likely100% | 10.6 | 18%8 lecture hrs | Balancedexam 14% · syllabus 18% | Rising | 3 recurring6 total | |
| 4 | U3Fundamental Concepts in Audio and Video | Very likely100% | 8.1 | 20%9 lecture hrs | Under-examinedexam 11% · syllabus 20% | Rising | 2 recurring4 total | |
| 5 | U5Multimedia Networks and Communication | Very likely88% | 9.3 | 11%5 lecture hrs | Balancedexam 11% · syllabus 11% | Steady | 3 recurring3 total | |
| 6 | U6Multimedia Information Retrieval and Content-Based Systems | Occasional0% | 0 | 9%4 lecture hrs | Under-examinedexam 0% · syllabus 9% | Steady | None |
Study smart, not hard
Drag the slider: studying the top 4 units in priority order covers ~89% of all observed marks.
- ~80% line
Lecture time vs exam marks
Where the exam pays more than the curriculum spends: ● lectures vs ● exam marks, as a share of the whole course. A long teal-leading bar = high-yield unit.