Maximum Frame Rate Video Acquisition Using Adaptive Compressed Sensing
Bibliographic record
Abstract
Compressed sensing is a novel technology to acquire and reconstruct sparse signals below the Nyquist rate. It has great potential in image and video acquisition to explore data redundancy and to significantly reduce the number of collected data. In this paper, we explore the temporal redundancy in videos, and propose a block-based adaptive framework for compressed video sampling. To address independent movement of different regions in a video, the proposed framework classifies blocks into different types depending on their inter-frame correlation, and adjusts the sampling and reconstruction strategy accordingly. Our framework also considers the diverse texture complexity of different regions, and adaptively adjusts the number of measurements collected for each region. The proposed framework also includes a frame rate selection module that selects the maximum achievable frame rate from a list of candidate frame rates under the hardware sampling rate and the perceptual quality constraints. Our simulation results show that compared to traditional raster scan, the proposed framework can increase the frame rate by up to six times depending on the scene complexity and the video quality constraint. We also observe a 1.5-7.8 dB gain in the average peak signal-to-noise ratio of the reconstructed frames when compared with prior works on compressed video sensing.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".