Perceptual screen content image quality assessment and compression
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
Compression of screen content has recently emerged as an active research topic due to the increasing demand in many applications such as wireless display and virtual desktop infrastructure. Screen content images (SCIs) exhibit different statistical properties in textual and pictorial regions, and the human visual system (HVS) also behaves differently when viewing the textual and pictorial regions in terms of the extent of visual field. Here we propose a perceptual SCI quality assessment approach that incorporates visual field adaptation and information content weighting. Furthermore, we propose a perceptual coding scheme in an attempt to optimize the HEVC Screen Content Coding encoder. Experimental results show that the proposed quality assessment method not only better predicts the perceptual quality of SCIs, but also leads to an effective way to optimize screen content coding schemes.
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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 | 0.001 | 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 | 0.001 |
| 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