The application of computer multimedia technology in film and television post-production
Bibliographic record
Abstract
Computer multimedia technology has brought unprecedented innovation to the film and television production industry.Multimedia technology in film and television post-production mainly focuses on two aspects of image processing and audio processing, this paper selects the skin color enhancement and voice enhancement for further research.Adaptive skin color enhancement method is proposed, IMCRA-OMLSA audio enhancement method is selected, and relevant experiments are designed to compare this paper's method with other classical skin color enhancement and voice enhancement methods respectively, and the effectiveness of this paper's method in skin color enhancement and audio enhancement is examined through the results of subjective and objective evaluation.The accuracy and F1 value of this paper's adaptive skin color detection method are 0.961 and 0.945, respectively, and the performance of skin color detection is good.The adaptive skin color detection method in this paper has the best performance with a comprehensive evaluation score of 6.81.In the objective evaluation of speech enhancement, the PESQ, STOI, WSS, and RMSE values of IMCRA-OMLSA method in this paper are 2.03, 72.36, and 38.06, respectively, which are all optimal results.On subjective evaluation, the MOS value of IMCRA-OMLSA method is 1.88 which is the highest value.IMCRA-OMLSA method has the best performance for speech enhancement.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".