Reduced-reference image quality assessment based on perceptual image hashing
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
Quality monitoring is of great importance for online media broadcasting service. Without access to the original reference image in most practical scenarios, reduced-referenced (RR) image quality assessment is a good tradeoff and generally more reliable than no-reference (NR) metrics. In this paper, we propose employing image hashing features as side information to estimate the image quality. With its monotone sensitivity to the content quality degradation (e.g. due to compression), the proposed RR quality monitoring method based on our FJLT (Fast Johnson-Lindenstrauss transform) hashing provides two advantages: the accurate image quality estimate in term of conventional objective quality measure such as PSNR, and the low data rate required for delivering the partial reference information. Experimental results demonstrate that the proposed hashing-based RR quality measure system can accurately estimate the quality degradation due to JPEG and JPEG2000, the two widely adopted compression techniques in nowadays network transmission services.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| 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