Bibliographic record
Abstract
All things considered, electronic imaging systems do not rival the human visual system despite notable progress over 40 years since the invention of the CCD. This work presents a method that allows design engineers to evaluate the performance gap between a digital camera and the human eye. The method identifies limiting factors of the electronic systems by benchmarking against the human system. It considers power consumption, visual field, spatial resolution, temporal resolution, and properties related to signal and noise power. A figure of merit is defined as the performance gap of the weakest parameter. Experimental work done with observers and cadavers is reviewed to assess the parameters of the human eye, and assessment techniques are also covered for digital cameras. The method is applied to 24 modern image sensors of various types, where an ideal lens is assumed to complete a digital camera. Results indicate that dynamic range and dark limit are the most limiting factors. The substantial functional gap, from 1.6 to 4.5 orders of magnitude, between the human eye and digital cameras may arise from architectural differences between the human retina, arranged in a multiple-layer structure, and image sensors, mostly fabricated in planar technologies. Functionality of image sensors may be significantly improved by exploiting technologies that allow vertical stacking of active tiers.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".