Combined Adaptive and Averaging Strategies for JPEG-Based Low Bit-Rate Image Coding
Bibliographic record
Abstract
A commonly recognized weak point of the DCT-based transform coding is its blocking effects which become increasingly visible in the low bit-rate territory. In the first part of this paper, motivated by a recent work of Bruckstein, Elad, and Kimmel (BEK) [1] and by the progress from [3], we propose a combined adaptive technique that can be applied to a BEK type of transform coding system for performance improvement. In the second part of the paper, motivated by a recent work of Tsaig, Elad, Milanfar, and Golub (TEMG) [2], we investigate an averaging technique for the design of optimal interpolation filter that can be utilized in a TEMG type system framework for further performance improvement. Simulation results are presented to demonstrate the effectiveness of the two proposed methods.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| 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".