Film Poster Design: Understanding Film Poster Designs and the Compositional Similarities within specific genres
Bibliographic record
Abstract
A film poster is an integral part of the promotion and marketing of an upcoming film. It encapsulates the essence of the film and is used in theatres, DVDs, and advertisements. For effective promotion, designers must create film posters that are relevant to the film they represent. This study explored whether film posters have common compositional aspects within genres. Previous research has explored genre classification and colour theory within film posters. However, the analysis of composition within film posters has been insufficiently researched in the context of recognizing patterns throughout film genres. I explored four film genres and analyzed existing film posters within these genres using a visual analysis tool. The analysis was then used to define a summarized composition of each genre, which then created generated posters using this analysis and definition. These generated posters consisted of low fidelity posters made using basic shapes such as circles and squares. The posters were used in participant interviews which followed a semi-structured approach. I asked participants questions to further their reflection about the posters and create discussion. Each participant was asked to label each poster with a genre based on their understanding of what they would assume to be the genre of the generated film poster. After analyzing the results from the participants’ decisions, the findings indicate that film posters have similar compositions within genres. These similarities are further recognized by film consumers and impact consumers’ perceptions of film posters.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".