L<inf>2</inf> restoration of L<inf>&#x221E;</inf>-decoded images with context modeling
Bibliographic record
Abstract
The L∞-constrained image coding is a technique to achieve substantially lower bit rate than strictly (mathematically) lossless image coding while still imposing a tight error bound at each pixel (colloquially referred to as near-lossless image coding). However, this technique becomes inferior in the L2distortion metric if the bit rate decreases further. We propose a new soft decoding approach to reduce the L2distortion of L∞-coded images, benefiting from the advantages of both minmax and mean square approximations. This is made possible by context modeling of quantization distortions and by exploiting the L∞bound inherent to near-lossless coding in a framework of image restoration. In addition, the proposed soft decoding approach offers an asymmetric high-fidelity image compression solution: the encoder is of low complexity with heavy computations of gaining coding efficiency performed by the decoder. Experimental results demonstrate that the new soft decoding approach can improve the PSNR of L∞-decoded images by more than 1 dB, and it can even outperform JPEG 2000 (a state-of-the-art encoder-optimized image codec) for bit rates higher than 1.17 bpp, while achieving much tighter L∞error bound.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".