LOW COMPLEXITY HEVC SCALABLE ENCODER BASED ON FSS ALGORITHM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it