AIM 2025 Challenge on Screen-Content Video Quality Assessment: Methods and Results
Bibliographic record
Abstract
This paper presents an overview of the AIM 2025 Challenge on Screen Content Video Quality Assessment. The challenge included a set of 150 source videos. To receive distorted versions, the source videos were transmitted through video conferencing applications, introducing real-world distortions such as compression artifacts and frame drops. Distorted versions were labeled by human crowd-sourcing assessors to receive reference subjective scores. The evaluation was based on subjective quality assessment via crowdsourcing, obtaining votes from over 8,000 assessors. The goal of the participants was to develop an algorithm to assess the visual quality of the videos, achieving the highest correlation with the subjective scores. The challenge attracted more than 45 registered teams, 5 of which passed the final phase with source code verification. The outcomes may provide insights into the state of the art in screen-content video quality assessment and highlight emerging trends and effective strategies in this evolving research area. All data, including the processed videos and subjective comparison votes and scores, is made publicly available – https://github.com/msu-video-group/AIM25_SC_Quality_Assessment
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".