Objective Quality Assessment for Color-to-Gray Image Conversion
Bibliographic record
Abstract
Color-to-gray (C2G) image conversion is the process of transforming a color image into a grayscale one. Despite its wide usage in real-world applications, little work has been dedicated to compare the performance of C2G conversion algorithms. Subjective evaluation is reliable but is also inconvenient and time consuming. Here, we make one of the first attempts to develop an objective quality model that automatically predicts the perceived quality of C2G converted images. Inspired by the philosophy of the structural similarity index, we propose a C2G structural similarity (C2G-SSIM) index, which evaluates the luminance, contrast, and structure similarities between the reference color image and the C2G converted image. The three components are then combined depending on image type to yield an overall quality measure. Experimental results show that the proposed C2G-SSIM index has close agreement with subjective rankings and significantly outperforms existing objective quality metrics for C2G conversion. To explore the potentials of C2G-SSIM, we further demonstrate its use in two applications: 1) automatic parameter tuning for C2G conversion algorithms and 2) adaptive fusion of C2G converted 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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".