High-Fidelity Image Compression for High-Throughput and Energy-Efficient Cameras
Bibliographic record
Abstract
We propose a new encoder-friendly image compression strategy for high-throughput cameras and other scenarios of resource-constrained encoders. The encoder performs $\ell_infty$-constrained predictive coding (DPCM coupled with uniform scalar quantizer), while the decoder solves an inverse problem of $\ell_2$ restoration of $\ell_\infty$-coded images. Although designed for minimum encoder complexity, the new codec outperforms the state-of-the-art encoder-centralized image codecs such as JPEG 2000 in PSNR for bit rates higher than 1.2 bpp, while maintaining much tighter $\ell_\infty$ error bounds as well. This is achieved through exploiting the tight error bound on each pixel naturally offered by the $\ell_\infty$-constrained encoder and by locally adaptive image modeling.
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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) | 0.000 | 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 | 0.001 |
| 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".