Capturing Banding in Images: Database Construction and Objective Assessment
Why this work is in the frame
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Bibliographic record
Abstract
With the fast technology advancement and the accelerated growth of high-quality image and video production and services, banding or false contour has become a frequently observed artifact in images, creating annoying negative impact on the visual quality-of-experience (QoE) of end users. Nevertheless, thorough investigations on the causes of banding, and effective and efficient methods to detect and reduce banding are largely lacking. This work targets at capturing and quantifying banding artifacts in images. We construct the first of its kind large-scale public database, consisting of 1,250 images with segmented banding regions and 169,501 image patches with class labels. We also develop a deep neural net-work based no-reference deep banding index (DBI), which not only produces an overall banding assessment of a given image, but also creates a banding map that indicates the variation of banding across the image space. Our experiments show that the proposed DBI method achieves accurate banding prediction with low computational cost. The database and the proposed algorithm are made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
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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 | 0.000 | 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