📝 H.323 protocol in multimedia communication (15 MCQs)

📖 Explanation: The quantizing phase replaces each DCT coefficient with a coarser value, discarding information that cannot be perfectly recovered. This loss of detail is what makes JPEG a lossy compression method, whereas the subsequent zigzag, run‑length, or arithmetic coding steps are lossless transformations.

📖 Explanation: Larger entries in the quantization matrix increase the divisor applied to each DCT coefficient, causing more coefficients to be rounded to zero. This reduces the amount of detail retained, leading to lower visual quality while also decreasing the file size because more zeros can be efficiently encoded.

📖 Explanation: The Q90 matrix contains larger quantization values than Q50, so it discards more high‑frequency information, producing a higher compression ratio but also more visible blocking and ringing artifacts. Conversely, Q50 retains more detail, resulting in a larger file but cleaner visual output.

📖 Explanation: Zigzag ordering places low‑frequency (often non‑zero) coefficients at the start and high‑frequency (often zero) coefficients at the end. This clustering of zeros creates longer consecutive zero runs, which run‑length coding can represent with fewer bits, thereby improving overall compression efficiency.

📖 Explanation: When many coefficients are zero, the symbol stream contains a dominant, highly predictable symbol. Predictable symbols reduce uncertainty, which directly lowers entropy. Lower entropy means fewer bits are required on average to encode the stream, explaining why JPEG achieves good compression after quantization.

📖 Explanation: Arithmetic coding models symbol probabilities more finely than run‑length coding, especially when symbol frequencies vary. By exploiting these probabilities, it can assign shorter codes to frequent symbols (like zeros) and longer codes to rare symbols, typically yielding a smaller compressed file compared with the simpler run‑length approach.

📖 Explanation: When the original pixel depth is larger (e.g., 20 bits), the DCT coefficients span a wider range. To reach comparable compression ratios, the quantization matrix must use larger divisors, effectively discarding more detail. Thus, higher bit depth demands larger quantization steps if the target file size is to remain similar.

📖 Explanation: Smooth‑gradient blocks contain little high‑frequency energy, so increasing quantization values for those coefficients turns many into zeros. Keeping low‑frequency values small preserves essential image structure. After this adaptive quantization, zigzag ordering clusters the resulting zeros, allowing run‑length or arithmetic coding to compress efficiently.

📖 Explanation: Even in a smoothly varying block, the DCT transforms spatial variations into frequency components. Most of the energy concentrates in low‑frequency coefficients, while higher‑frequency coefficients are small. Quantization uses larger divisors for high frequencies, causing those small values to round to zero, which explains the abundance of zeros despite the lack of sharp edges.

📖 Explanation: Arithmetic coding leverages the statistical probability of symbols; with a high zero probability (70 %), it can assign extremely short codes to zeros and longer codes to rare non‑zeros. This fine‑grained probability exploitation usually surpasses the fixed pattern of run‑length coding, which may not capture the exact distribution as efficiently.

📖 Explanation: The Q90 matrix contains larger quantization factors, aggressively reducing coefficient precision, which dramatically lowers file size (higher compression) but also removes fine details, increasing visible distortion. Conversely, Q10 uses smaller factors, preserving more detail at the cost of a larger file. Therefore, Q90 gives higher compression but more distortion.

📖 Explanation: Entropy coding seeks to assign shorter codes to more frequent symbols. After zigzag ordering, zeros dominate the stream, making their probability high. Arithmetic coding dynamically models these probabilities, generating variable‑length codes that on average are shorter than any fixed‑length scheme, thereby achieving better compression.

📖 Explanation: Only the first few positions in the zigzag order correspond to low‑frequency DCT coefficients. The presence of a non‑zero value at the first position (12) and another at the fourth position (5) indicates that low‑frequency energy is present, while the long run of zeros suggests that high‑frequency components were negligible or eliminated by quantization.

📖 Explanation: Increasing the precision of high‑frequency coefficients (by using smaller quantization values) reduces the likelihood that those coefficients round to zero. Fewer zeros mean longer run‑lengths or less favorable probability distributions for arithmetic coding, which directly reduces compression efficiency.

📖 Explanation: Adaptive quantization selects larger quantization values for low‑variance (flat) blocks, turning many coefficients to zero and achieving higher compression. For high‑variance (textured) blocks, smaller values preserve detail, reducing perceptual distortion. This balance yields overall better compression without sacrificing visual fidelity where it matters most.