Transparent composite model for large scale image/video processing
Bibliographic record
Abstract
This paper aims to tackle theoretical modeling and dimension reduction, two fundamental issues in large scale image/video data processing, together, by proposing a transparent composite model (TCM) for transformed image/video data. Specifically, to handle the heavy tail phenomenon commonly seen in Discrete Cosine Transform (DCT) coefficients of image/video data, a TCM first separates the tail of a sequence of DCT coefficients from the main body of the sequence. Then, a parametric distribution is used to model the main body while a uniform distribution is used to model the tail. Efficient online algorithms for establishing a TCM are proposed and proved to converge exponentially fast, which suits large-scale image/video data processing. It is also demonstrated that a TCM has an inherent non-linear data reduction capability - DCT coefficients of an image in the heavy tail identified by a TCM reveal some unique global features of the image while being insignificant statistically. This, together with its fast convergence, makes the proposed model a desirable choice for modeling DCT coefficients in large-scale image/video applications, such as online quantization design, entropy coding design, and image/video analytics in Big Data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".