Edge adaptive intra field de-interlacing of video images
Bibliographic record
Abstract
Expanding image by an arbitrary scale factor and thereby creating an enlarged image is a crucial image processing operation. De-interlacing is an example of such operation where a video field is enlarged in vertical direction with 1 to 2 scale factor. The most advanced de-interlacing algorithms use a few consequent input fields to generate one output frame. In order to save hardware resources in video processors, missing lines in each field may be generated without reference to the other fields. Line doubling, known as “bobbing”, is the simplest intra field de-interlacing method. However, it may generate visual artifacts. For example, interpolation of an inserted line from a few neighboring lines by vertical filter may produce such visual artifacts as “jaggies.” In this work we present edge adaptive image up-scaling and/or enhancement algorithm, which can produce “jaggies” free video output frames. As a first step, an edge and its parameters in each interpolated pixel are detected from gradient squared tensor based on local signal variances. Then, according to the edge parameters including orientation, anisotropy and variance strength, the algorithm determines footprint and frequency response of two-dimensional interpolation filter for the output pixel. Filter’s coefficients are defined by edge parameters, so that quality of the output frame is controlled by local content. The proposed method may be used for image enlargement or enhancement (for example, anti-aliasing without resampling). It has been hardware implemented in video display processor for intra field de-interlacing of video images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".