An embedded wavelet image coder with parallel encoding and sequential decoding of bit-planes
Bibliographic record
Abstract
Wavelet based coders are widely used in image and video compression. Many popular embedded wavelet coders are based on a data structure known as zerotree. However, there exists a category of embedded wavelet coders that are fast and efficient even without zerotrees. These coders are based on three key concepts: (1) wavelet coefficient reordering; (2) bit-plane partition; and (3) encoding of bit-planes with efficient run-length coding. In this paper, we propose a bit-plane encoder that can be used in these non-zerotree algorithms. Instead of encoding the bit-planes sequentially, the bit-plane encoding process can be completed in one pass when multiple bit-plane encoders are used simultaneously. This bit-plane encoder is inherently suitable for parallel processing architecture. The decoding process is treated sequentially since each bit-plane stream can only be synchronized upon the correct decoding of higher bit-planes. To the best of our knowledge, this paper is the first to realize parallelization through encoding multiple bit-planes simultaneously.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".