Image quality assessment of 2-chip color camera in comparison with 1-chip color and 3-chip color cameras in various lighting conditions: initial results
Bibliographic record
Abstract
A 2-chip color camera, named UNB Super-camera, is introduced in this paper. Its image qualities in different lighting conditions are compared with those of a 1-chip color camera and a 3-chip color camera. The 2-chip color camera contains a high resolution monochrome (panchromatic) sensor and a low resolution color sensor. The high resolution color images of the 2-chip color camera are produced through an image fusion technique: UNB pan-sharp, also named FuzeGo. This fusion technique has been widely used to produce high resolution color satellite images from a high resolution panchromatic image and low resolution multispectral (color) image for a decade. Now, the fusion technique is further extended to produce high resolution color still images and video images from a 2-chip color camera. The initial quality assessments of a research project proved that the light sensitivity, image resolution and color quality of the Super-camera (2-chip camera) is obviously better than those of the same generation 1-chip camera. It is also proven that the image quality of the Super-camera is much better than the same generation 3-chip camera when the light is low, such as in a normal room light condition or darker. However, the resolution of the Super-camera is the same as that of the 3- chip camera, these evaluation results suggest the potential of using 2-chip camera to replace 3-chip camera for capturing high quality color images, which is not only able to lower the cost of camera manufacture but also significantly improving the light sensitivity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".