A 60-Frames/s CMOS Image Sensor With Pixelwise Conversion Gain Modulation and Self-Triggered ADCs for Per-Frame Adaptive DCG-HDR Imaging
Bibliographic record
Abstract
CMOS image sensors (CISs) have been evolving rapidly in recent years, offering unprecedented imaging capabilities. For high-end mobile CIS products, high dynamic range (HDR) features are favorably desired. Among various HDR techniques, dual-conversion-gain (DCG)-based HDR imaging offers several advantages due to its high image quality and single-frame basis. For DCG-based HDR applications, however, current state-of-the-art CISs suffer from frame rate reduction and higher power consumption. In this article, we present an adaptive DCG-HDR imaging based on a CIS design with per-frame pixelwise conversion gain (CG) modulation and self-triggered analog-to-digital converters (ADCs). According to the scene to be captured, each pixel adaptively adjusts to its unique CG mode. With a single readout per frame and without image fusion, a proof-of-concept prototype CIS that operates in adaptive DCG-HDR mode achieves a 90.5 dB of dynamic range and a power figure of merit (FoM) of 9.9 nJ/frame$\cdot $pixel. Compared to DCG-HDR imaging, operating at 60 frames/s, the presented adaptive DCG-HDR imaging reduces CIS power consumption by 38% and enables single-frame pixelwise HDR imaging, which is suitable for future mobile CIS products.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".