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 paper<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>This 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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".