A new reduced-reference metric for measuring spatial resolution enhanced images
Bibliographic record
Abstract
Assessment of image quality is critical for many image processing algorithms, such as image acquisition, compression, restoration, enhancement, and reproduction. In general, image quality assessment algorithms are classified into three categories: full-reference (FR), reduced-reference (RR), and no-reference (NR) algorithms. The design of NR metrics is extremely difficult and little progress has been made. FR metrics are easier to design and the majority of image quality assessment algorithms are of this type. A FR metric requires the reference image and the test image to have the same size. This may not the case in real life of image processing. In spatial resolution enhancement of hyperspectral images, such as pan-sharpening, the size of the enhanced images is larger than that of the original image. Thus, the FR metric cannot be used. A common approach in practice is to first down-sample an original image to a low resolution image, then to spatially enhance the down-sampled low resolution image using a subject enhancement technique. In this way, the original image and the enhanced image have the same size and the FR metric can be applied to them. However, this common approach can never directly assess the image quality of the spatially enhanced image that is produced directly from the original image. In this paper, a new RR metric was proposed for measuring the visual fidelity of an image with higher spatial resolution. It does not require the sizes of the reference image and the test image to be the same. The iterative back projection (IBP) technique was chosen to enhance the spatial resolution of an image. Experimental results showed that the proposed RR metrics work well for measuring the visual quality of spatial resolution enhanced hyperspectral images. They are consistent with the corresponding FR metrics.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".