Evidence that Viewers Prefer Higher Frame-Rate Film
Bibliographic record
Abstract
High frame-rate (HFR) movie-making refers to the capture and projection of movies at frame rates several times higher than the traditional 24 frames per second. This higher frame rate theoretically improves the quality of motion portrayed in movies, and helps avoid motion blur, judder, and other undesirable artifacts. However, there is considerable debate in the cinema industry regarding the acceptance of HFR content given anecdotal reports of hyper-realistic imagery that reveals too much set and costume detail. Despite the potential theoretical advantages, there has been little empirical investigation of the impact of high frame-rate techniques on the viewer experience. In this study, we use stereoscopic 3D content, filmed and projected at multiple frame rates (24, 48, and 60 fps), with shutter angles ranging from 180° to 358°, to evaluate viewer preferences. In a paired-comparison paradigm, we assessed preferences along a set of five attributes (realism, motion smoothness, blur/clarity, quality of depth, and overall preference). The resulting data show a clear preference for higher frame rates, particularly when contrasting 24 fps with 48 or 60 fps. We found little impact of shutter angle on viewers' choices, with the exception of one measure (motion smoothness) for one clip type. These data are the first empirical evidence of the advantages afforded by high frame-rate capture and presentation in a cinema context.
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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 | 0.002 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".