Bit allocation for lossy image set compression
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
Large sets of similar images are produced in many applications. To store these images more efficiently, redundancy among similar images need to be exploited. A number of methods have been proposed to reduce such inter-image redundancy in lossy image set compression. These methods encode each image either using a conventional image compression algorithm, or predicts the image from a similar image already encoded and encode the prediction residual. Although these methods differ in the way they determine the prediction structure in the image set, they do not consider the effect of bit allocation on the overall quality of the reconstructed images. In this paper, we show that Lagrangian optimization can be used to determine bit allocation for each encoded image in order to improve the overall quality of the reconstructed image set. Furthermore, a model approximating rate-distortion curves of the residual images can be used to reduce the encoding time significantly.
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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 | 0.001 | 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