Image Degradation in Time Due to Interacting Hot Pixels
Bibliographic record
Abstract
The number of hot pixels (induced by cosmic rays) in digital imaging sensors increases as the camera ages, with rates strongly dependent on pixel size. This increasing number of defects results in a higher probability that two defects will occur within a 5x5 pixel square. When this occurs, the currently employed color demosaicing and JPEG image compression algorithms spread the damage into a 16x16 pixel area, creating significant color changes resulting in noticeable image degradation. Experiments show, for example, that a 20 Mpixel DSLR camera needs only 127 hot pixels to generate a P=4.6% probability of an image degradation in 1.4 years, and P increases with the time squared. In addition, this probability grows inversely with the pixel size squared. We show that the time for a given probability of image degradation varies linearly with the square of the pixel size, inversely with both the square root of the imager area and the ISO. This accelerated image degradation in time will have a significant impact on the lifetime of cameras that are expected to provide accurate images for a long period of time, e.g., cameras in self-driving cars. Experimental tests have identified multiple interacting hot pixels in several cameras.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".