A Low Power CMOS Imaging System with Smart Image Capture and Adaptive Complexity 2D-DCT Calculation
Bibliographic record
Abstract
A novel low power CMOS imaging system with smart image capture and adaptive complexity 2D-Discrete Cosine Transform (DCT) is proposed. Compared with the existing imaging systems, it involves the smart image capture and image processing stages cooperating together and is very efficient. The type of each 8 × 8 block is determined during the image capture stage, and then input into the DCT block, along with the pixel values. The 2D-DCT calculation has adaptive computation complexity according to block types. Since the block type prediction has been moved to the front end, no extra time or calculation is needed during image processing or image capturing for prediction. The image sensor with block type decision circuit is implemented in TSMC 0.18 µm CMOS technology. The adaptive complexity 2D-DCT compression is implemented based on Cyclone EP1C20F400C8 device. The performance including the image quality of the reconstructed picture and the power consumption of the imaging system are compared to those of traditional CMOS imaging systems to show the benefit of the proposed low power algorithm. According to simulation, up to 46% of power consumption can be saved during 2D DCT calculation without extra loss of image quality for the reconstructed pictures compared with the conventional compression methods.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".