Block-based adaptive compressed sensing for video
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
Compressed sensing is a novel technology to acquire and reconstruct signals below the Nyquist rate, and has great potential in image and video acquisition to explore the data redundancy and to significantly reduce the number of sampled data. In this paper, we explore the temporal redundancy in videos, and propose a block-based adaptive framework for compressed video sampling. It addresses the independent movement of different regions in a video, classifies blocks into different types depending on their inter-frame correlation, and adjusts the sampling and reconstruction strategies accordingly. Our framework also considers the diverse texture complexity of different regions, and adaptively adjusts the number of measurements collected for each region based on their sparsity. Our simulation results show that the proposed framework reduces the number of sampled measurements by 52% to 80% while still satisfying the quality constraint on the reconstructed frames. Compared to prior works, our proposed scheme improves the quality of the reconstructed frames and achieves a 0.8dB to 5.4dB gain in the average PSNR.
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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 | 0.000 | 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 it