Content adaptive enhancement of video images
Bibliographic record
Abstract
Digital video products such as TVs, set-up boxes and DV players have circuits that enhance quality of incoming video content. User may control parameters of these circuits according to video source for optimum quality. However, there is a need for a procedure that can adjust these parameters automatically without user interaction. A three stages method for content adaptive enhancement of video images (CAEVI) in display processors is proposed. The first stage measures video signal statistics such as intensity and frequency histograms over image’s active area. The following stage generates control parameters for image processing blocks after measured statistics analysis. One of four quality classes (low, medium, high or special) is assigned to the incoming video, and a set of predefined control parameters for this class is selected. At the third stage, the set of control parameters is applied to the corresponding image processing blocks to reduce noise, improve signal transitions, enhance spatial details, contrast, brightness and saturation, and resample the video image. Video signal statistics are measured and accumulated for each frame, and control parameters are gradually adjusted on scene basis. Measuring and processing blocks are implemented in hardware to provide real time response. Image analysis and quality classification algorithm is implemented in embedded software for flexibility. The proposed method has been implemented in video processor as “Auto HQV” feature. The method was originally developed for TVs and. It is currently under adaptation for hand held devices.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".