Influence of image compression on the quality of UNB pan-sharpened imagery: a case study with security video image frames
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
UNB Pan-sharp, also named FuzeGo, is an image fusion technique to produce high resolution color satellite images by fusing a high resolution panchromatic (monochrome) image and a low resolution multispectral (color) image. This is an effective solution that modern satellites have been using to capture high resolution color images at an ultra-high speed. Initial research on security camera systems shows that the UNB Pan-sharp technique can also be utilized to produce high resolution and high sensitive color video images for various imaging and monitoring applications. Based on UNB Pansharp technique, a video camera prototype system, called the UNB Super-camera system, was developed that captures high resolution panchromatic images and low resolution color images simultaneously, and produces real-time high resolution color video images on the fly. In a separate study, it was proved that UNB Super Camera outperforms conventional 1-chip and 3-chip color cameras in image quality, especially when the illumination is low such as in room lighting. In this research the influence of image compression on the quality of UNB Pan-sharped high resolution color images is evaluated, since image compression is widely used in still and video cameras to reduce data volume and speed up data transfer. The results demonstrate that UNB Pan-sharp can consistently produce high resolution color images that have the same detail as the input high resolution panchromatic image and the same color of the input low resolution color image, regardless the compression ratio and lighting condition. In addition, the high resolution color images produced by UNB Pan-sharp have higher sensitivity (signal to noise ratio) and better edge sharpness and color rendering than those of the same generation 1-chip color camera, regardless the compression ratio and lighting condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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