Region-of-interest Image Coding for Satellite Application Based on Saliency of Visual Attention
Bibliographic record
Abstract
To satisfy the demand for high-fidelity remote sensing image transmission in the limited channel, a Region-of-interest (ROI) image coding algorithm based on the saliency of visual attention is proposed. Firstly, the CCSDS coding framework can reduce the computing complexity, which is suitable for real-time applications (such as satellite application). Secondly, an automatic ROI extraction algorithm, which is based on the saliency of visual attention, is proposed. Finally, the ROI is determined by using the mask and can be used for arbitrary shape. The experimental results have demonstrated that, compared with the algorithm in JPEG2000, the proposed algorithm is easy to be implemented. Moreover, it has good subjective visual quality, which is consistent with the human visual perception. Meanwhile, the proposed algorithm could be a ROI algorithm used for CCSDS standard.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".