Image compression through optimized linear mapping and parametrically generated features
Bibliographic record
Abstract
In this paper a new linear mapping scheme for image compression is proposed. The main objective is to construct an asymptotic approximation to higher order surface fitting schemes previously reported in [1]. Each block of the image is independently reconstructed from a set of "features" through a linear mapping. These features should be (ideally) independent or at least uncorrelated to benefit the info-max principle. A random sequence generator is employed, using a sine function with two parameters, to approach such a requirement for the features. These two parameters and the set of linear weights are found through an optimization process. An off-line training phase is first performed to find the weights used in the linear mapping. These weights are then used to compress images during the on-line phase where the sine function parameters are found and quantized. The computation time is excessive due to nonlinear optimization required. However, thanks to quantization, a look-up table can be implemented to overcome this disadvantage. The proposed structure is fairly robust to the random sequence length as experimentally demonstrated.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".