LOW COMPLEXITY HEVC SCALABLE ENCODER BASED ON FSS ALGORITHM
Bibliographic record
Abstract
High efficiency video coding (HEVC) provides compressed video bit streams with good quality video that offers an extension of its kind-Scalable extension of high efficiency video coding (SHVC). SHVC offers delivery of compressed video bit streams over various types of networks with broader scalability. Mode decision and motion search in standard HEVC is more exhaustive and complex, and it is even more complex in the HEVC scalable extension. The encoder time increases due to the computational complexity in SHVC to find the best mode in the search algorithm. To overcome this, a forward-looking step search (FSS) algorithm is proposed to offer less computational complexity with acceptable coding efficiency. The FSS algorithm searches for the optimal matching block by fixing nine points around the assumed center point. The optimal block is determined by forward-looking each point that has minimal rate distortion cost (RDC). The simulation results show that the algorithm works better with a 0.5% increase in the peak signal-to-noise ratio (PSNR) and an encoder time savings of 21.16% with the standard SHVC SHM software.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".