YUV luma/chroma quantization and sparse correspondence for real time video stereo matching
Bibliographic record
Abstract
To realize the real time dense disparity map running at a video rate of 30 fps, the dynamic time warp algorithm (DTW) provides a robust method of stereo matching. This method requires to calculate the pixel-by-pixel similarity matrix that indicates the similarity from one pixel of the left image to a pixel of the right image in the raster profile. The size of the similarity matrix S is N2 for the raster size N. Down sampling to reduce N defeats the purpose of disparity measurement because the spatial resolution is also reduced. A method to introduce sparse samples is proposed in this paper, so that the proposed method reduces N but not sacrificing the spatial resolution of stereo matching. The denoised raster waveform is coarsely quantized to produce a binary train at each of the quantization level. The run-length for the contiguous one's is used as a feature to represent the raster waveform profile. The size of the sparse set is about N = 30 as opposed to the raster size N = 352 for the CIF size image. This idea is applied to the luma Y-image and the chroma UV-image in the YUV color space to take advantage of the concept of image segmentation by color in addition to the stereo matching normally performed in the gray scale.
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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 | 0.000 | 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 | 0.000 |
| 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".