An Adaptive High-Fidelity Image Compression Framework for Internet of Vehicles
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
This paper proposes a new adaptive high-fidelity image compression solution to achieve a high compression ratio with the least distortion using a generative adversarial network. This work focuses on preserving the details by compressing the salient regions in the image with a high bit rate to guarantee the generation of high-quality outputs that sustain most of its characteristics. The image background is compressed with a lower bit rate. This work is tested against the Kodak, CLIC, MOTS, and UADTV datasets based on the bit-per-pixel rate where the results prove that our work achieves the highest quality with the lowest rate. To achieve a lower bit rate, the arithmetic coding algorithm is applied to the compression sequence which reduces the rate by 35%. With the achieved low bit rate, our work boosts the rate of image transmission by a factor of more than 2.
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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 | 0.007 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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