Image foveation based on vector quantization
Bibliographic record
Abstract
Summary form only given. The perceptual resolution of vision is greatly space variant and is highest at the point of fixation and decreases rapidly away from this point. Novel unstructured and structured vector quantization (VQ) schemes are proposed to take advantage of this property of the human visual system (HVS) by providing the best image quality around the fixation point. As a foveation technique, the unstructured VQ does not enjoy progressive transmission in the sense that any request of improvement in the ROI is always replied by complete retransmission of image vectors with a higher resolution VQ scheme. The structured VQ, which is based on a residual vector quantizer (RVQ), yields an embedded progressive bit stream. The main idea for the RVQ foveation is to use more quantization stages for image vectors closer to the fixation point. This method allows the receiver to change its fixation point without any waste of transmitted information and to have multiple fixation points. This quantization strategy enables gradual image resolution change from the ROI to the background.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".