Distortion estimation for reference frame modification methods
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
Due to the transmission of encoded video over error prone channels, using error resilient techniques at the encoder has become an essential issue. These techniques try to decrease the impact of transmission errors by using different approaches such as inserting Intra MacroBlocks (MBs), changing the prediction structure, or considering the channel state in selecting the best MB modes. In this work, we make use of the channel aware mode decision scheme used in the Loss Aware Rate Distortion Optimization (LARDO) method while simultaneously using the prediction structure of the Improved Generalized Source Channel Prediction (IGSCP) technique. In order to combine these two schemes, we estimate the end-to-end distortion for the IGSCP prediction structure in the H.264/AVC encoder. Simulation results, using the JSVM software, demonstrate the effectiveness of our technique for different sequences.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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