A Method Based on Local Variance for Quality Assessment of Multiresolution Image Fusion
Bibliographic record
Abstract
Several methods exist to combine a panchromatic image of high spatial resolution with lower resolution multispectral imagery. Of particular interest are those methods designed to simulate real multispectral images having the spatial resolution of a panchromatic image. To help justify an algorithm over another one, quantitative evaluation of the quality of a fused image is necessary. In most cases, the evaluation is performed with the original high- and low-spatial resolution images degraded to a coarser resolution by pixel-block averaging. The multispectral image of the highest resolution serves as a reference image (real image). Most approaches proposed for quality assessment are based on statistical measures computed over the whole image; typical measures are the correlation coefficient and the root-mean-square deviation. However, these measures make no reference to the spatial domain. In this paper, we suggest measures based on local variance computed over a three-by-three pixel window as complementary measures to evaluate the quality of the fused images. The rationale is that an ideal fused image must replicate the variance of the reference image when estimated locally. To help discriminate between local variance induced by real details as opposed to artefacts, the variance is partitioned into two terms. Each term takes into consideration the expected direction of the added details over the multispectral image oversampled by pixel replication. The method is illustrated with different fusion models applied to an Ikonos image.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".