Modifying weber fraction law to postprocessing and edge detection applications
Bibliographic record
Abstract
A postprocessing scheme is proposed to enhance compressed images. The main objective is to obtain improvements that are pertinent to the properties of human visual system. The proposed scheme implements Weber fraction (also called contrast sensitivity) to enhance the appearance of the current block by incorporating information from adjacent blocks. The ratio DeltaI/I is found between the mean of a line of pixels in the current block and two points, each resides on the boundary of an adjacent block, that are the continuation of the chosen line. To avoid biasing toward low intensity values and to preserve the symmetry of the sensitivity curve, I was replaced by the maximum of the actual mean value and the corresponding value of the negative image or simply max(mean, 255-mean). If DeltaI/I is less than a threshold, the chosen line is replaced with a one fitting the original data and the two boundary points. Although PSNR improvement is <0.3 dB, the resultant image is visually more pleasing as will be demonstrated experimentally. The algorithm can be easily modified to perform as an edge detection scheme by finding DeltaI/I between any pixel and its 8 neighbours. The maximum is then taken. A new histogram thresholding is then applied to discriminate the edge pixels. Experimental results indicate a superior capability of the proposed scheme to detect edges of objects that are close in intensity to their background. Some comparisons with Sobel operator are also 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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".