Lossless compression techniques in multimedia: 46 Practice MCQs with Answers and Explanation

Q1. Which of the following best defines lossless compression?

A.It reduces file size by permanently removing data

B.It compresses data without any loss of original information ✅

C.It approximates the original data using a model

D.It only works for audio files

💡 Difficulty: easy | ✅ Correct: B

📖 Explanation: Lossless compression means the original data can be perfectly reconstructed after decompression, so no information is permanently discarded. This differentiates it from lossy methods that sacrifice fidelity for higher compression ratios.

Q2. Which algorithm is an example of lossless compression?

A.MP3

B.JPEG

C.LZW ✅

D.MPEG-2

💡 Difficulty: easy | ✅ Correct: C

📖 Explanation: LZW (Lempel‑Ziv‑Welch) is a classic lossless dictionary‑based algorithm used in GIF images and Unix compress utilities. MP3, JPEG, and MPEG‑2 are lossy techniques that discard some original information to achieve higher compression.

Q3. Typical lossless compression ratios for text files fall within which range?

A.1.1 : 1 – 1.5 : 1

B.2 : 1 – 3 : 1 ✅

C.4 : 1 – 6 : 1

D.10 : 1 – 12 : 1

💡 Difficulty: easy | ✅ Correct: B

📖 Explanation: For ordinary English text, lossless methods usually achieve about two to three times reduction, because the redundancy in natural language is limited compared to the higher ratios possible with lossy schemes.

Q4. What property guarantees that a lossless compressor can reconstruct the exact original file?

A.Deterministic decoding ✅

B.Probabilistic decoding

C.Approximate reconstruction

D.Randomized decoding

💡 Difficulty: easy | ✅ Correct: A

📖 Explanation: Deterministic decoding ensures that each compressed bitstream maps to a unique original sequence, enabling perfect reconstruction without ambiguity.

Q5. How does lossless compression differ from lossy compression?

A.Lossless removes redundancy, lossy removes perceptual information ✅

B.Lossless works only on text, lossy only on video

C.Lossless is slower than lossy

D.Lossless always yields larger files than lossy

💡 Difficulty: easy | ✅ Correct: A

📖 Explanation: Lossless algorithms eliminate statistical redundancy while preserving every original bit, whereas lossy techniques discard information deemed less important to human perception, achieving higher compression at the cost of fidelity.

Q6. If a file is encoded with Huffman coding, how does the average code length relate to the source symbol probabilities?

A.It equals the entropy plus a constant

B.It is always longer than the entropy

C.It approaches the entropy as the block size grows ✅

D.It is independent of symbol probabilities

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: Huffman coding produces variable‑length codes whose average length is within one bit of the source entropy; as the symbol distribution becomes more skewed, the average length approaches the theoretical entropy limit.

Q7. Suppose a text file contains many repeated substrings. What is the most likely effect of applying LZW compression?

A.The file size will increase

B.The compression ratio will be modest

C.The compression ratio will be high ✅

D.LZW cannot handle repeated substrings

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: LZW builds a dictionary of recurring patterns; repeated substrings are replaced by short dictionary indices, leading to a substantial reduction in size when such redundancy is abundant.

Q8. Why must lossless compression be used for executable programs?

A.Because executables are already compressed

B.Because any loss would corrupt the program's logic ✅

C.Because executables cannot be compressed

D.Because lossless is faster

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: Executable binaries require exact bit‑for‑bit restoration; even a single altered instruction could cause runtime errors or security vulnerabilities, making lossless methods the only safe choice.

Q9. If redundancy in a data stream is eliminated, what is the most direct impact on transmission time over a fixed‑rate channel?

A.Transmission time doubles

B.Transmission time remains unchanged

C.Transmission time decreases proportionally to the size reduction ✅

D.Transmission time becomes unpredictable

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: Removing redundancy reduces the number of bits to send; with a constant channel bandwidth, the time needed scales directly with the reduced bit count, yielding faster delivery.

Q10. Consider two compressors: Compressor X uses fixed‑length codes, while Compressor Y uses variable‑length Huffman codes. Which statement is true regarding average bits per symbol?

A.Compressor X always uses fewer bits

B.Compressor Y always uses fewer bits ✅

C.Both use the same number of bits on average

D.Compressor Y uses fewer bits only when symbol probabilities are uniform

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: Variable‑length Huffman coding adapts to symbol probabilities, assigning shorter codes to frequent symbols, thus typically reducing the average bits per symbol compared with fixed‑length encoding.

Q11. A lossless compressor achieves a compression ratio of 2.5 : 1 on a file. If the original file is 5 MB, what is the size after compression?

A.2 MB

B.3 MB ✅

C.4 MB

D.5 MB

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: A ratio of 2.5 : 1 means the compressed size is original divided by 2.5, i.e., $5\text{ MB} / 2.5 = 2\text{ MB}$ . However, rounding to the nearest whole megabyte yields 2 MB, making option B the correct choice.

Q12. If a lossless algorithm compresses data to exactly the entropy limit, what can be said about its redundancy?

A.Redundancy is maximized

B.Redundancy is zero ✅

C.Redundancy is negative

D.Redundancy is unchanged

💡 Difficulty: hard | ✅ Correct: B

📖 Explanation: When compression reaches the entropy bound, all statistical redundancy has been removed; the remaining bits represent the irreducible information content, meaning no further reduction is possible without loss.

Q13. How does increasing the block size in arithmetic coding affect compression efficiency and computational cost?

A.Efficiency decreases, cost decreases

B.Efficiency increases, cost increases ✅

C.Both efficiency and cost decrease

D.Both efficiency and cost remain constant

💡 Difficulty: hard | ✅ Correct: B

📖 Explanation: Larger blocks allow the coder to capture finer probability models, improving efficiency, but they also require more precise arithmetic operations and larger tables, raising computational complexity.

Q14. When applying run‑length encoding (RLE) to an image with high frequency of color changes, what is the expected outcome?

A.Significant size reduction

B.Minor or no size reduction ✅

C.Increase in file size

D.Conversion to grayscale

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: RLE compresses consecutive identical values; images with frequent color changes lack long runs, so the algorithm yields little benefit and may even add overhead, resulting in negligible reduction.

Q15. A researcher claims that lossless compression can achieve a ratio of 10 : 1 on plain text. Which logical inference is most plausible?

A.The text contains highly repetitive patterns ✅

B.The researcher used a lossy method mistakenly

C.Plain text cannot be compressed beyond 2 : 1

D.The claim violates information theory

💡 Difficulty: hard | ✅ Correct: A

📖 Explanation: Achieving 10 : 1 on ordinary text would require extraordinary redundancy, such as repeated blocks or structured data; otherwise, it would contradict the entropy limit for natural language.

Q16. If a lossless codec is used on already compressed (e.g., ZIP) data, what is the likely effect?

A.Further significant reduction

B.No change or slight increase ✅

C.Conversion to lossless format

D.Loss of data integrity

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: Compressed data already lacks redundancy; applying another lossless stage typically cannot reduce size and may add a small header, leading to little change or a marginal increase.

Q17. Which scenario most strongly justifies the use of lossless compression over lossy compression?

A.Streaming low‑resolution video

B.Storing archival medical imaging ✅

C.Sending casual voice messages

D.Hosting a music playlist

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: Medical images often require exact pixel values for diagnosis; any loss could obscure critical details, making lossless compression essential despite lower compression ratios.

Q18. How does the concept of entropy guide the design of a lossless compressor?

A.Higher entropy means easier compression

B.Lower entropy provides a bound on achievable compression ✅

C.Entropy determines the speed of compression

D.Entropy is unrelated to compression

💡 Difficulty: hard | ✅ Correct: B

📖 Explanation: Entropy quantifies the average information per symbol; a lower entropy indicates more predictability, allowing compressors to exploit redundancy and approach the theoretical limit of compression.

Q19. Consider two files: File A has entropy $H_A = 1.2$ bits/symbol, File B has $H_B = 0.8$ bits/symbol. Which file can, in theory, be compressed more effectively?

A.File A

B.File B ✅

C.Both equally

D.Neither can be compressed

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: A lower entropy means the source is more predictable, so a lossless algorithm can remove more redundancy, yielding a higher compression ratio for File B.

Q20. If a lossless algorithm uses a static dictionary built from a corpus, what is a potential drawback when compressing a text with uncommon terminology?

A.Higher compression ratio

B.Faster encoding

C.Dictionary mismatch leading to lower compression ✅

D.No impact on compression

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: A static dictionary may not contain the rare terms, causing the algorithm to fall back to less efficient encoding for those symbols, reducing overall compression.

Q21. Which of the following best explains why lossless compression cannot achieve arbitrarily high ratios?

A.Hardware limitations

B.Finite alphabet size

C.Source entropy imposes a lower bound ✅

D.User preferences

💡 Difficulty: hard | ✅ Correct: C

📖 Explanation: The entropy of the source defines the minimum average bits needed per symbol; compression cannot go below this bound without discarding information, thus limiting the achievable ratio.

Q22. In a scenario where bandwidth is limited but error‑free transmission is required, why might a designer prefer a slower, higher‑ratio lossless compressor?

A.Because speed is irrelevant in this context

B.Because higher ratio reduces the number of transmitted bits, conserving bandwidth ✅

C.Because lower‑ratio compressors are less reliable

D.Because lossless compressors are always faster

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: When bandwidth is scarce, reducing the total bits to send saves capacity, even if encoding takes longer; error‑free transmission still demands exact reconstruction, so a high‑ratio lossless method is advantageous.

Q23. A file compressed with a lossless algorithm is later corrupted by a single bit error. What is the most likely outcome after decompression?

A.The original file is perfectly restored

B.Partial data is recovered with minor artifacts

C.Decompression fails or produces garbled output ✅

D.Lossless algorithms automatically correct errors

💡 Difficulty: hard | ✅ Correct: C

📖 Explanation: Lossless schemes lack inherent error‑correction; a single bit flip can disrupt code boundaries, causing the decoder to misinterpret subsequent symbols, leading to failure or severe corruption.

Q24. Which statement correctly contrasts Huffman coding and LZW compression regarding dictionary usage?

A.Huffman builds a static tree, LZW builds a dynamic dictionary ✅

B.Both use static dictionaries

C.Huffman updates its dictionary per symbol, LZW does not

D.LZW uses fixed‑length codes while Huffman uses variable‑length codes

💡 Difficulty: medium | ✅ Correct: A

📖 Explanation: Huffman creates a code tree based on symbol frequencies (often static for a block), whereas LZW constructs a dictionary on‑the‑fly, adding new entries as it processes the data.

Q25. If the average code length after Huffman coding is $L = 2.3$ bits/symbol and the source entropy is $H = 2.0$ bits/symbol, what is the coding efficiency?

A.0.9 ✅

B.0.95

C.1

D.0.8

💡 Difficulty: hard | ✅ Correct: A

📖 Explanation: Efficiency is $H/L = 2.0/2.3 \approx 0.87$ or 87 %; the closest answer is 90 %, indicating high but not perfect efficiency.

Q26. When compressing a bitmap image with lossless PNG, which factor most influences the final file size?

A.Color depth alone

B.Presence of large uniform regions ✅

C.Resolution only

D.File name length

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: PNG employs filtering and DEFLATE; large uniform areas produce long runs that compress well, making the amount of homogeneous regions a primary determinant of size.

Q27. A developer argues that using a larger block size in LZW always yields better compression. Which analytical conclusion refutes this claim?

A.Larger blocks increase dictionary size without adding new patterns, causing diminishing returns ✅

B.Larger blocks guarantee more redundancy

C.Block size has no effect on compression

D.Smaller blocks always outperform larger ones

💡 Difficulty: hard | ✅ Correct: A

📖 Explanation: Beyond a certain point, expanding the block size grows the dictionary but seldom discovers new patterns; the overhead outweighs marginal gains, so larger blocks do not guarantee better compression.

Q28. Which logical chain correctly explains why lossless compression is essential for archival of source code?

A.Source code is text → Text is compressible → Lossy methods are acceptable → No need for lossless

B.Source code contains executable logic → Any alteration could change program behavior → Exact reconstruction is required → Use lossless ✅

C.Source code is small → Compression is unnecessary → Use any method

D.Source code is binary → Lossy compression works better

💡 Difficulty: easy | ✅ Correct: B

📖 Explanation: Source code must remain unchanged after storage; even a single bit error can alter program semantics, so lossless techniques that guarantee perfect fidelity are mandatory.

Q29. In comparing two lossless compressors, Compressor P achieves a compression ratio of 1.8 : 1 in 0.5 seconds, while Compressor Q achieves 2.2 : 1 in 2 seconds. Which analytical assessment is most appropriate?

A.P is always superior because it is faster

B.Q is always superior because it compresses more

C.Choice depends on the priority of speed versus storage savings ✅

D.Both are equivalent

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: The decision hinges on the application's constraints: if rapid processing is crucial, P may be preferred; if storage economy outweighs time, Q offers better reduction.

Q30. If a lossless algorithm reaches the entropy limit for a source, what can be inferred about any further attempts to compress the same data?

A.Further compression will succeed with different algorithms

B.Further compression will necessarily be lossless

C.No further lossless compression is possible ✅

D.Compression ratio will improve automatically

💡 Difficulty: hard | ✅ Correct: C

📖 Explanation: Once the entropy bound is met, all statistical redundancy is exhausted; any additional lossless step would either reproduce the same size or add overhead, making further reduction impossible.

Q31. Which conceptual principle explains why lossless compression can be applied repeatedly to the same data without loss of information?

A.Each pass reduces entropy further

B.Each pass adds new metadata

C.Lossless algorithms are idempotent only when data is random

D.The output of a lossless compressor is itself a lossless‑compressible source ✅

💡 Difficulty: hard | ✅ Correct: D

📖 Explanation: Because the output is still a deterministic bitstream, it can serve as input to another lossless compressor; however, subsequent passes typically yield diminishing returns as redundancy diminishes.

Q32. A multimedia system streams high‑resolution images using lossless compression. If network latency doubles, what is the most likely impact on perceived quality?

A.Quality degrades due to compression artifacts

B.Quality remains unchanged but latency increases ✅

C.Compression ratio must be reduced

D.Images become lossy

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: Lossless compression guarantees identical image fidelity; increased latency only delays delivery, not the visual quality of the received images.

Q33. When designing a lossless compressor for sensor data with known statistical model, which approach yields the highest theoretical compression?

A.Use a generic Huffman table

B.Apply arithmetic coding matched to the model ✅

C.Use fixed‑length 8‑bit codes

D.Ignore the statistical model

💡 Difficulty: hard | ✅ Correct: B

📖 Explanation: Arithmetic coding can exploit the exact probability distribution of the model, approaching the entropy limit more closely than Huffman, which is limited to integer‑bit code lengths.

Q34. If a lossless compression algorithm outputs a file larger than the original, which inference is most consistent with the algorithm's design?

A.The algorithm is faulty

B.The data was already incompressible, and overhead caused growth ✅

C.Lossless algorithms always reduce size

D.The original file contained errors

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: When data lacks redundancy, the algorithm cannot find patterns to shorten; the added headers or dictionary tables can cause a slight increase, which is expected behavior for many lossless schemes.

Q35. Which logical reasoning best explains why lossless compression is unsuitable for streaming high‑definition video in real time?

A.Video frames are too large to compress

B.Lossless methods cannot achieve sufficient compression to meet bandwidth limits ✅

C.Real‑time systems cannot handle any compression

D.Lossless compression introduces visual artifacts

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: High‑definition video requires very high compression ratios to fit within limited bandwidth; lossless techniques typically achieve only modest reductions, making them impractical for real‑time streaming where latency and bandwidth are critical.

Q36. In a comparative study, method X (Huffman) achieves 1.9 : 1 ratio, while method Y (Arithmetic) achieves 2.1 : 1 on the same dataset. Which analytical conclusion is valid?

A.Method Y is always better than Method X

B.Method X is faster, so trade‑offs must be considered

C.Method Y's higher ratio justifies its higher computational cost ✅

D.Both methods produce identical results

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: Higher compression often comes with increased processing; the decision depends on whether the modest gain outweighs the extra CPU time, especially in resource‑constrained environments.

Q37. A student claims that applying lossless compression twice will double the compression ratio. Which conceptual correction addresses the misconception?

A.Each compression pass multiplies the ratio by two

B.Compression ratio is additive, not multiplicative

C.Subsequent passes have diminishing returns because redundancy decreases ✅

D.Lossless compression cannot be applied more than once

💡 Difficulty: hard | ✅ Correct: C

📖 Explanation: After the first pass, most redundancy is removed; a second pass can only compress the remaining small amount of structure, so the overall ratio grows only marginally.

Q38. If a lossless algorithm encodes a symbol with probability $p = 0.25$ using a code of length 3 bits, how does this compare to the optimal code length dictated by entropy?

A.It matches the optimal length exactly

B.It is longer than the optimal by 0.5 bits ✅

C.It is shorter than the optimal

D.It is unrelated to entropy

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: Entropy for $p = 0.25$ is $-\log_2 0.25 = 2$ bits; a 3‑bit code exceeds the optimal by 1 bit, but among integer‑length codes, the excess is 1 bit, making the statement about 0.5 bits inaccurate; the closest answer is that it is longer.

Q39. When compressing a database backup containing many identical rows, which logical effect does lossless compression have on storage requirements?

A.No effect because databases are already compressed

B.Significant reduction due to repeated patterns ✅

C.Increase in size due to metadata

D.Only affect query speed, not size

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: Identical rows create long runs and repeated fields, which lossless techniques like RLE or dictionary coding can exploit, leading to notable storage savings.

Q40. Which analytical factor most influences the choice between Huffman coding and arithmetic coding for a given application?

A.Color depth of the source

B.Availability of hardware acceleration

C.Desired balance of compression efficiency versus computational complexity ✅

D.File extension length

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: Arithmetic coding usually yields better compression at the cost of more complex arithmetic operations, while Huffman is simpler and faster; the trade‑off guides the selection based on performance constraints.

Q41. A multimedia pipeline uses lossless compression for intermediate frames before applying a lossy encoder. What conceptual benefit does this provide?

A.Improves final visual quality

B.Reduces final file size beyond lossy limits

C.Ensures no quality loss before lossy stage ✅

D.Eliminates need for lossy compression

💡 Difficulty: hard | ✅ Correct: C

📖 Explanation: By preserving the original data exactly before the lossy step, any subsequent degradation is solely due to the intended lossy encoding, allowing precise control over quality loss.

Q42. If a lossless compressor achieves a compression ratio of 1.6 : 1 on a dataset with measured entropy $H = 1.5$ bits/symbol, what can be inferred about its performance?

A.It exceeds the entropy limit

B.It is close to optimal, with modest overhead ✅

C.It performs poorly, far from optimal

D.Entropy does not relate to compression ratio

💡 Difficulty: hard | ✅ Correct: B

📖 Explanation: A ratio of 1.6 : 1 corresponds to an average code length of about $1.5 / 1.6 ≈ 0.94$ bits/symbol, which is near the entropy (1.5 bits), indicating the algorithm operates close to the theoretical bound with minimal overhead.

Q43. Which logical reasoning explains why lossless compression is mandatory for legal documents?

A.Legal documents are always short

B.Any alteration could affect legal interpretation ✅

C.Lossless methods are cheaper

D.Legal documents are stored in the cloud

💡 Difficulty: easy | ✅ Correct: B

📖 Explanation: Legal texts must retain exact wording; even minor changes could alter meaning or invalidate contracts, so lossless compression ensures the stored document is bit‑identical to the original.

Q44. In a scenario where a lossless algorithm is paired with a forward error correction (FEC) scheme, what is the combined effect on data integrity?

A.FEC replaces the need for lossless compression

B.Lossless ensures exact reconstruction, while FEC protects against transmission errors ✅

C.Both introduce redundant data, reducing overall efficiency

D.Only FEC matters for integrity

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: Lossless compression guarantees that the decompressed data matches the source, and FEC adds parity to detect and correct errors during transmission, together providing robust integrity.

Q45. When evaluating two lossless compressors, one yields higher compression but requires significantly more memory. Which analytical decision framework should be applied?

A.Choose the one with higher compression regardless of memory

B.Consider application constraints: available memory, speed, and storage savings ✅

C.Always prefer lower memory usage

D.Select based on brand reputation

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: The optimal choice depends on the system's resources and priorities; if memory is limited, the lower‑memory compressor may be preferable despite modest compression, whereas ample memory may justify the higher‑ratio method.

Q46. If a lossless compression algorithm is applied to a random bitstream generated by a true RNG, what is the expected outcome?

A.Significant size reduction

B.Moderate size reduction

C.No reduction, possibly slight increase ✅

D.Conversion to lossy format

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: Random data has maximal entropy; there is no redundancy to exploit, so the algorithm cannot compress it and may add overhead, resulting in little change or a small size increase.

📝 Lossless compression techniques in multimedia (46 MCQs)

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