Existing and emerging image quality metrics
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
This paper summarizes and evaluates some of the existing methods of measuring and quantifying the quality of a digital image. Unfortunately, no general method has been found. The performance of a quality metric is normally gauged by its prediction accuracy, monotonicity and consistency. It is also expected to mirror the quality scores assigned by independent human observers. Research to this point has generally focused on full-reference (FR) measures that assume that coded and original images are available. Often times, an original is not easily obtainable, or perhaps does not even exist. Therefore, researchers have recently shown a great deal of interest in developing reduced-reference (RR) and no-reference (NR) metrics. This study implements and compares some of the most common IQMs and seeks to determine if there is any difference in their performance. Analysis of the results focuses on determining if any IQM is superior to the others over a general set of test images.
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 | 0.000 | 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