A window-sequence coding transformation suitable for computationally-efficient bit-serial implementation of stack filters
Bibliographic record
Abstract
A window-sequence coding (WSC) technique suitable for improving the computational efficiency of a bit-serial implementation of 2-D stack filters is proposed. The WSC technique takes advantage of the observation that in most images, many pixels appearing in the filter-window at a certain time-instant assume non-distinct values. An algorithm that uses the bit-serial binary-tree search (BTS) architecture for stack filtering and employs the WSC technique is developed. The proposed algorithm is designated as a modified binary-tree search (MBTS) algorithm. It is shown that the computational efficiency of MBTS algorithm for 2-D stack filtering is significantly better than the efficiency of a BTS algorithm employing a recently proposed technique called the input compression. The improvement stems from the fact that in the input compression method, the samples appearing in a filter-window of size M are always mapped to the set of integers (0, 1, ..., M-1), in spite of the fact that usually in a 2-D window, several groups of pixels assume non-distinct values.
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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 | 0.001 | 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 | 0.000 | 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".