AKTU Data Compression · Important Questions 2026Source: ryzenstudy.com PYQs
BCS064 Data Compression Important Questions 2026 (AKTU)
Repeat analysis + most-asked questions, unit-wise
What we did: built from all 4 AKTU Data Compression papers (2021-22–2024-25): 95 questions grouped into 60 distinct questions, 23 of which were asked in more than one paper.
- of marks were repeat questions
- 60%
- of marks were repeat questions
- questions asked in 2+ papers
- 23
- questions asked in 2+ papers
- papers analysed (2021-22–2024-25)
- 4
- papers analysed (2021-22–2024-25)
- units, mapped to current syllabus
- 5
- units, mapped to current syllabus
How much of the paper repeats?
- Repeats are real: when we checked each newer BCS064 paper against the papers before it, on average 60% of its marks were questions already asked in an earlier paper.
- 2023-24 paper: 77% of marks were repeats (10 of 17 repeated questions were in our top list).
- 2024-25 paper: 44% of marks were repeats (7 of 10 repeated questions were in our top list).
- Topic weightage is a guide, not a guarantee: our Tier 1 + Tier 2 topics covered 70% of the next paper's marks — picking the same number of topics at random would have covered about 61%.
Across 10 subjects and 23 papers we tested, 43% of marks repeated earlier questions on average.
1.Unit-wise most-asked questions
Ranked by how many papers asked them, then by how recently. Click a paper to open it.
Unit 1: Compression Techniques
19% of past marks| Question | Asked |
|---|---|
| What is data compression? Explain with the help of a block diagram. | Asked 3× |
| What do you mean by a uniquely decodable code? Determine whether the following codes are uniquely decodable or not: (i) {0,01,11,111} (ii) {0,01,110,111} (iii) {1,10,110,111} (iv) {0,01,10} | Asked 2× |
| Explain modeling and coding with the help of examples. What do you understand by a prefix code? Explain with an example. | Asked 2× |
| Write a short note on: i. Entropy ii. Compression ratio iii. Prefix code iv. Run-length encoding (RLE) v. Signal-to-noise ratio (SNR) 7 marks2024-25 Q4(b) | Asked 1× |
Unit 2: The Huffman coding algorithm
19% of past marks| Question | Asked |
|---|---|
| Determine the Golomb code representations of 9 and 13 when m=5. | Asked 3× |
| What is the principle behind Huffman coding? | Asked 2× |
| Explain Rice coding and its implementation. | Asked 2× |
| Draw the Huffman tree for the following symbols whose frequency of occurrence in a message text is given along with the symbol: A:15, B:6, C:7, D:12, E:25, F:4, G:6, H:10, I:15. Find the Huffman code and average code length. | Asked 2× |
Unit 3: Dictionary Techniques
24% of past marks| Question | Asked |
|---|---|
| Describe the function of the Burrows-Wheeler transform in compression. | Asked 3× |
| Describe the steps involved in the basic algorithm for Prediction with Partial Match (PPM). | Asked 3× |
| A sequence is encoded using the LZW algorithm with the initial dictionary shown in the table (see original paper). The output of the LZW encoder is the following sequence (see original paper). Decode this sequence. | Asked 3× |
| Differentiate between the LZ77 and LZ78 dictionary compression approaches. | Asked 2× |
Unit 4:
18% of past marks| Question | Asked |
|---|---|
| Describe the types of quantizers: uniform, adaptive, and non-uniform, with examples. | Asked 3× |
| List the distortion criteria commonly used in quantization and explain their importance. | Asked 3× |
| What do you understand by adaptive quantization? Explain the various approaches to adapting the quantizer parameters. | Asked 3× |
| What do you understand by a uniform quantizer? How is uniform quantization of uniformly distributed sources and of non-uniform sources done? | Asked 3× |
Unit 5:
19% of past marks| Question | Asked |
|---|---|
| Explain the working of the Linde-Buzo-Gray algorithm for vector quantization. | Asked 4× |
| Describe the advantages of vector quantization over scalar quantization. | Asked 2× |
| Explain structured vector quantization and pyramid vector quantization. | Asked 2× |
| Write the advantages of tree structured vector quantization. | Asked 2× |
2.Topic weightage: which topics carry the marks
Blue = Tier 1 + Tier 2 (topics that carried ~80% of past marks). Grey = the rest. It shows where marks have gone, not a promise of what comes next.
Huffman algorithm · U2
8.7% (cum 9%)
LZ77 and LZ78 · U3
7.6% (cum 16%)
LBG algorithm · U5
7.2% (cum 23%)
PPM · U3
6.5% (cum 30%)
Quantization problem · U4
5.9% (cum 36%)
Vector vs scalar quantization · U5
5.3% (cum 41%)
Adaptive quantization · U4
4.9% (cum 46%)
Uniquely decodable and prefix codes · U1
4.2% (cum 50%)
Compression overview · U1
4.1% (cum 54%)
Distortion criteria · U4
3.9% (cum 58%)
Burrows-Wheeler transform · U3
3.7% (cum 62%)
Golomb codes · U2
3.7% (cum 66%)
Uniform quantizer · U4
3.7% (cum 69%)
Structured VQ · U5
3.7% (cum 73%)
Information theory basics · U1
3.2% (cum 76%)
Compression models · U1
3.1% (cum 79%)
Tree structured VQ · U5
3.1% (cum 83%)
Rice codes · U2
2.7% (cum 85%)
Modeling and coding · U1
2.5% (cum 88%)
Adaptive Huffman · U2
2% (cum 90%)
Measures of performance · U1
1.9% (cum 92%)
Binary (arithmetic) coding · U3
1.8% (cum 93%)
Dictionary applications · U3
1.8% (cum 95%)
Static and digram dictionary · U3
1.4% (cum 97%)
Tunstall codes · U2
1.2% (cum 98%)
Facsimile encoding · U3
1.2% (cum 99%)
Applications of Huffman coding · U2
1.2% (cum 100%)
| Topic | Papers | Share |
|---|---|---|
| Huffman algorithm · Unit 2 · Tier 1 | 4/4 | 8.7% |
| LZ77 and LZ78 · Unit 3 · Tier 1 | 4/4 | 7.6% |
| LBG algorithm · Unit 5 · Tier 1 | 4/4 | 7.2% |
| PPM · Unit 3 · Tier 1 | 4/4 | 6.5% |
| Quantization problem · Unit 4 · Tier 1 | 3/4 | 5.9% |
| Vector vs scalar quantization · Unit 5 · Tier 1 | 3/4 | 5.3% |
| Adaptive quantization · Unit 4 · Tier 1 | 3/4 | 4.9% |
| Uniquely decodable and prefix codes · Unit 1 · Tier 1 | 3/4 | 4.2% |
| Compression overview · Unit 1 · Tier 2 | 3/4 | 4.1% |
| Distortion criteria · Unit 4 · Tier 2 | 4/4 | 3.9% |
| Burrows-Wheeler transform · Unit 3 · Tier 2 | 3/4 | 3.7% |
| Golomb codes · Unit 2 · Tier 2 | 3/4 | 3.7% |
| Uniform quantizer · Unit 4 · Tier 2 | 3/4 | 3.7% |
| Structured VQ · Unit 5 · Tier 2 | 3/4 | 3.7% |
| Information theory basics · Unit 1 · Tier 2 | 3/4 | 3.2% |
| Compression models · Unit 1 · Tier 2 | 3/4 | 3.1% |
| Tree structured VQ · Unit 5 · Tier 2 | 3/4 | 3.1% |
| Rice codes · Unit 2 · Tier 3 | 2/4 | 2.7% |
| Modeling and coding · Unit 1 · Tier 3 | 2/4 | 2.5% |
| Adaptive Huffman · Unit 2 · Tier 3 | 2/4 | 2% |
| Measures of performance · Unit 1 · Tier 3 | 3/4 | 1.9% |
| Binary (arithmetic) coding · Unit 3 · Tier 3 | 2/4 | 1.8% |
| Dictionary applications · Unit 3 · Tier 3 | 2/4 | 1.8% |
| Static and digram dictionary · Unit 3 · Tier 3 | 1/4 | 1.4% |
| Tunstall codes · Unit 2 · Tier 3 | 1/4 | 1.2% |
| Facsimile encoding · Unit 3 · Tier 3 | 1/4 | 1.2% |
| Applications of Huffman coding · Unit 2 · Tier 3 | 2/4 | 1.2% |
3.Paper pattern (2024-25)
| Section | Questions | Rule |
|---|---|---|
| A | 7 × 2 marks | Attempt all |
| B | 6 × 7 marks | Attempt any 3 |
| C | 10 × 7 marks | Attempt any 1 |
4.In the syllabus, never asked yet
Low priority, but read them once — a new paper can always start here.
- Minimum variance Huffman · U2
- Bi-level image compression · U3
- Quantization models · U4
- Non-uniform quantization · U4
5.Study order
- 1Huffman algorithm (Unit 2, Tier 1)
- 2LZ77 and LZ78 (Unit 3, Tier 1)
- 3LBG algorithm (Unit 5, Tier 1)
- 4PPM (Unit 3, Tier 1)
- 5Quantization problem (Unit 4, Tier 1)
- 6Vector vs scalar quantization (Unit 5, Tier 1)
- 7Adaptive quantization (Unit 4, Tier 1)
- 8Uniquely decodable and prefix codes (Unit 1, Tier 1)
- 9Compression overview (Unit 1, Tier 2)
- 10Distortion criteria (Unit 4, Tier 2)
- 11Burrows-Wheeler transform (Unit 3, Tier 2)
- 12Golomb codes (Unit 2, Tier 2)
- 13Uniform quantizer (Unit 4, Tier 2)
- 14Structured VQ (Unit 5, Tier 2)
- 15Information theory basics (Unit 1, Tier 2)
- 16Compression models (Unit 1, Tier 2)
- 17Tree structured VQ (Unit 5, Tier 2)
FAQ
Which units of BCS064 carry the most marks?
Unit 3 (Dictionary Techniques, 24% of past marks) and Unit 2 (The Huffman coding algorithm, 19% of past marks).
Do AKTU Data Compression questions repeat?
Yes. On average 60% of the marks in each newer BCS064 paper came from questions already asked in an earlier paper, and 23 questions have appeared in more than one paper.
How is this list made?
Every question from the 4 papers (2021-22–2024-25) was mapped to the current AKTU syllabus and grouped with the same question from other years. Questions are ranked by how many papers they appeared in, then by how recently.
Study this subject
Note: this is based on past AKTU papers, not a guarantee. Questions are mapped to the current syllabus; topics that are no longer in the syllabus are left out.