A 60-Frames/s CMOS Image Sensor With Pixelwise Conversion Gain Modulation and Self-Triggered ADCs for Per-Frame Adaptive DCG-HDR Imaging
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
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<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it