Dithered soft decision quantization for baseline JPEG encoding and its joint optimization with huffman coding and quantization table selection
Bibliographic record
Abstract
Based on baseline JPEG, a new image coding framework is first developed, where dithered (uniform) quantizers are used to replace JPEG uniform quantizers for the purpose of improving the rate-distortion performance without sacrificing the coding complexity instead of the conventional subjective image quality. By combining dithering with soft decision quantization (SDQ)-yielding dithered SDQ, an iterative algorithm is then proposed for jointly designing dithers for DCT coefficients at each frequency (i.e., dither table), quantization table, run-length coding, and Huffman coding. The algorithm converges in the sense that its rate-distortion cost is monotonically decreasing until a stationary point is reached. When compared with state-of-the-art baseline JPEG R-D optimizer proposed recently by Yang and Wang, our algorithm achieves comparable and sometimes better rate-distortion performance with 65% computational complexity reduction.
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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 | 0.002 |
| Open science | 0.000 | 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 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".