A 120dB Dynamic Range CMOS Image Sensor with Dual Programmable Conversion Gains and Pixelwise QCG Modulation for Single-Frame Adaptive QCG-HDR Imaging
Bibliographic record
Abstract
At present, single-frame high-dynamic-range (HDR) imaging technologies are favorable in high-end CMOS image sensor (CIS) products. When applying conversion-gain (CG) based single-frame HDR technologies such as dual-CG (DCG) HDR imaging, however, the fixed CG ratio and the multi-time pixel readouts make CISs suffer from a limited dynamic range extension and higher power consumption. In this paper11This research work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC)., we introduce a quad-CG (QCG) CIS design which is capable of dual-CG programming and pixelwise QCG modulation. With an on-chip CG programmer (CGP) and pixel CG modulators (PCMs), the proposed CIS enables adaptive QCG-HDR imaging with a flexible dynamic range extension. A prototype CIS chip with 640x512 pixels demonstrated dual-CG programming and extended the dynamic range up to 120dB. When applying adaptive QCG-HDR imaging at a frame rate of 60fps, the CIS and the companion image signal processor (ISP) achieved a power figure of merit (FoM) of 7.0nJ/frame·pixel. The proposed adaptive QCG-HDR imaging is a low-power quad-gain (LPQG) single-frame HDR solution for mobile imaging applications.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".