GPU-aided motion adaptive video deinterlacing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In most applications, video deinterlacing has to be performed in real time. Numerous algorithms have been developed to strike a good balance between throughput and quality. The motion adaptive deinterlacing algorithm switches between two modes: direct merging of two fields in areas of no motion, or intrafield adaptive interpolation when motions are detected. In this paper, we propose a fast GPU-aided implementation of a motion adaptive deinterlacing algorithm using NVIDIA CUDA (Compute Unified Device Architecture) technology. We discuss the techniques of adapting the computations in motion detection and adaptive directional interpolation to the GPU architecture for maximum video throughput possible. The objective is to fully utilize the processing power of GPU without compromising the visual quality of the deinterlaced video. Experimental results are reported and discussed to demonstrate the performance of the proposed GPU-aided motion adaptive video deinterlacer in both speed and visual quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it