📝 Admission control in IntServ (8 MCQs)
📖 From Data Communication and Networks • 30. Quality of Service • 8 questions available
What is Admission control in IntServ?
Admission control in IntServ is performed hop-by-hop during the reservation setup phase, where each router checks its local resource availability against the requested flow specification and only confirms the reservation if it can guarantee the specified service level.
📝 All Admission control in IntServ MCQs
Q1. If a JPEG image uses 24 bits per pixel and a GIF image uses 8 bits per pixel, the theoretical size ratio is 3:1. However, the text states GIF reduces size by a factor of 3 compared to JPEG. Which inference best explains why the observed reduction matches the theoretical ratio?
📖 Explanation: The reduction aligns with the bit‑depth difference because GIF replaces each 24‑bit color with an 8‑bit palette index. The palette indexing itself accounts for a three‑fold size drop, assuming no extra overhead from headers or metadata. Hence, the observed factor matches the theoretical ratio derived from pixel representation alone.
Q2. Considering the quantization step with Q50 and subsequent zigzag reordering, which statement best compares their impact on compression efficiency?
📖 Explanation: Quantization directly influences visual quality by discarding less important high‑frequency data, while zigzag reorders the remaining coefficients to create longer runs of zeros. This ordering enhances the performance of entropy coders (e.g., Huffman), making compression more efficient without further affecting the image’s perceived quality.
Q3. After mapping true colors to a 256‑color palette in a GIF, which compression technique would most likely yield the greatest additional reduction, and why?
📖 Explanation: Arithmetic coding adapts to the exact probability distribution of palette indices, often achieving compression ratios closer to the theoretical limit than static methods. Because GIF images can exhibit non‑uniform symbol frequencies, modeling these probabilities yields the most reduction, especially when combined with the already compact palette representation.
Q4. A video stream consists of 30 frames per second, each frame is a 640×480 image with 24‑bit color. If the network bandwidth is limited to 10 Mbps, which conclusion follows about the feasibility of uncompressed transmission?
📖 Explanation: Each frame contains 640 × 480 = 307,200 pixels; at 24 bits per pixel this is 7,372,800 bits per frame. Multiplying by 30 fps yields roughly 221 Mbps, far above the 10 Mbps limit. Therefore, transmitting the video uncompressed is impossible without either reducing resolution, frame rate, or applying compression.
Q5. Given two lossless compression schemes for GIF images—dictionary coding and arithmetic coding— which analysis correctly describes their performance in terms of compression ratio and computational complexity?
📖 Explanation: Arithmetic coding continuously refines probability models, often achieving superior compression ratios compared with dictionary methods that rely on fixed pattern tables. However, this adaptability incurs greater computational overhead, making arithmetic coding more CPU‑intensive, whereas dictionary coding typically runs faster with modest compression gains.
Q6. How does increasing the quantization values in the JPEG Q50 matrix affect both the compression ratio and the type of visual artifacts observed?
📖 Explanation: Larger quantization steps discard more high‑frequency DCT coefficients, boosting the compression ratio but introducing noticeable block‑type artifacts as adjacent 8×8 blocks become less correlated. The resulting image often appears coarse or blocky, especially in areas with fine detail, reflecting the trade‑off between size and visual fidelity.
Q7. If a GIF image uses a custom palette of 128 colors instead of the full 256, what is the impact on file size and why?
📖 Explanation: A smaller palette reduces the size of the palette table stored in the file header. Pixel data still occupies one byte per pixel, so the main savings come from the reduced 128‑entry table rather than a per‑pixel bit‑rate change, yielding a modest overall file‑size decrease.
Q8. In JPEG compression, what does the term 'zigzag ordering' refer to?
📖 Explanation: Zigzag ordering is the process of scanning the 8×8 block of quantized DCT coefficients in a diagonal pattern that starts at the top‑left corner and moves toward the bottom‑right. This creates a one‑dimensional sequence that groups low‑frequency values first and high‑frequency values later, facilitating run‑length and entropy coding.