📝 Video compression techniques (16 MCQs)
📖 From Data Communication and Networks • 28. Multimedia • 16 questions available
What is Video compression techniques?
Video compression techniques reduce the massive data volume of moving pictures by exploiting spatial redundancy within individual frames and temporal redundancy between consecutive frames using standards like MPEG and H.264, allowing high-quality video to be streamed or stored at practical bitrates by encoding only the changes and essential visual information rather than every raw pixel.
📝 All Video compression techniques MCQs
Q1. What does the run‑length coding method replace in a repeated sequence?
📖 Explanation: The method substitutes each run of identical symbols with two items: the number of repetitions (the count) and the symbol itself. This dual representation captures the length of the run and the value that repeats, enabling compression by reducing repetitive information.
Q2. In the example 'AAABBBBCDDDDDDEEE → 3A4B1C6D3E', what is the count associated with the symbol D?
📖 Explanation: The compressed form shows '6D', indicating that six consecutive D symbols were present in the original string. Recognizing the count directly from the encoded pair confirms that the correct answer is the number six.
Q3. A binary pattern has length 20 bits. After run‑length coding using 4‑bit counts, the compressed representation uses 12 bits. What is the compression ratio (original/compressed)?
📖 Explanation: Compression ratio is calculated as original size divided by compressed size. Here, 20 bits ÷ 12 bits ≈ 1.667, which matches the second option. This ratio indicates the original data is about 1.67 times larger than the compressed version.
Q4. Pattern X = 111000111000 (12 bits) and Pattern Y = 101010101010 (12 bits). Using 4‑bit counts for zeros between ones, which pattern achieves a greater compression ratio?
📖 Explanation: Pattern X yields two zero‑run counts (3 and 3), requiring 2 × 4 = 8 bits for counts, giving a ratio of 12/8 = 1.5. Pattern Y produces five counts, needing 5 × 4 = 20 bits, which exceeds the original length, so its ratio is below 1. Therefore, Pattern X provides the higher ratio.
Q5. If a binary pattern contains a run of 20 consecutive zeros between two ones, and the encoder uses 4‑bit fixed‑length counts, what problem arises?
📖 Explanation: A 4‑bit field can represent values only up to 2⁴‑1 = 15. A run of 20 zeros exceeds this limit, causing the count to overflow. The decoder would read an incorrect value, leading to a corrupted reconstruction of the original pattern.
Q6. Given a compressed binary string '1100001100001000' that uses 4‑bit counts for zeros between ones, how many zeros are represented between the first and second ones?
📖 Explanation: The string begins with a '1' followed by the 4‑bit count '1000'. Interpreting '1000' as a binary number yields 8 in decimal, meaning eight zeros separate the first and second ones.
Q7. When a data stream consists of long runs of the same symbol, how does run‑length coding compare to a method that encodes each symbol individually?
📖 Explanation: Run‑length coding replaces each long run with a count and the symbol, dramatically reducing the number of stored items. Encoding each symbol separately would store every individual occurrence, so RLC achieves a substantial size reduction for such data.
Q8. If the fixed‑length count field is increased from 4 bits to 8 bits, what is the expected effect on the compression ratio for the same binary pattern?
📖 Explanation: An 8‑bit field can describe runs up to 255 symbols, allowing longer sequences to be captured without splitting them into multiple counts. This reduces the number of count fields needed, which typically improves the compression ratio despite the larger field size.
Q9. How does the modified run‑length coding for binary patterns differ from standard run‑length coding applied to general alphabets?
📖 Explanation: For binary data where one symbol dominates, the method stores only the count of the dominant symbol (e.g., zeros) occurring between occurrences of the other symbol (e.g., ones). Standard RLC records counts for every symbol, making the binary variant more compact for such skewed data.
Q10. Consider a binary pattern of length 64 bits with an average run length of 2 zeros between ones. Using a 4‑bit count field, which statement best describes the storage requirement compared to the original?
📖 Explanation: Each run consumes 4 bits for its count, while the original representation uses roughly 3 bits per run (2 zeros + 1 one). Because 4 > 3, the compressed representation requires more bits overall, so the compressed size exceeds the original.
Q11. Given the original string '000111000111' and its run‑length encoded form '3 0 3 1 3 0 3 1', how many total bits are saved if each count and symbol is stored using 4 bits?
📖 Explanation: The original string occupies 12 bits. The encoded form contains four pairs; each pair uses 4 bits for the count and 4 bits for the symbol, totaling 32 bits. Since 32 > 12, the encoding actually increases storage, resulting in no savings.
Q12. Apply run‑length coding to the string 'CCCCCCDDDDDAA' and give the encoded result.
📖 Explanation: Counting consecutive identical symbols yields six C's, five D's, and two A's. Concatenating each count with its symbol gives the encoded string '6C5D2A', which matches the first option.
Q13. Why does the binary‑only version of run‑length coding store only the count of zeros between ones when zeros are more frequent than ones?
📖 Explanation: When zeros dominate, recording the number of zeros between the rarer ones avoids repeatedly storing the abundant symbol. This approach reduces the amount of data needed to describe the pattern, leading to better compression efficiency.
Q14. If a binary pattern alternates between 0 and 1 (e.g., 010101...), what is the expected impact on compression ratio using the 4‑bit count method?
📖 Explanation: Alternating bits produce runs of length 1, requiring a 4‑bit count for each run. The overhead of a 4‑bit field for every single zero outweighs any savings, so the compressed data is roughly the same size or slightly larger than the original.
Q15. Design an algorithm step that decodes a compressed binary pattern where each 4‑bit count represents zeros between successive ones. Which step correctly reconstructs the original sequence?
📖 Explanation: Decoding must mirror the encoding: for each 4‑bit count, generate the specified number of zeros, then write a one to mark the end of the run. Repeating this process reconstructs the original binary pattern accurately.
Q16. How would you adapt run‑length coding to handle data with three symbols (e.g., 0,1,2) while keeping the compression efficient?
📖 Explanation: A practical extension records counts for the dominant symbol (the one appearing most often) between occurrences of the other two symbols, while the less frequent symbols are stored directly. This balances compression benefit with manageable metadata.