Shot Structure Analysis for Pre-Release Video Success Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video success prediction analytics rely on subjective measures and are not available until after video release, lacking objective data from the video's own raw assets to inform pre-release decision-making.
Innovation Solution
The system analyzes the shot structure of a video by creating a hierarchy of shot clusters and superclusters based on content similarities, using algorithms like DBSCAN to generate statistics that are input into predictive models, such as ratios of superclusters and hierarchy depth, to estimate video success metrics like survival rates and click-throughs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If existing pre-release analytics are used, then video success prediction is available before release, but the results are biased due to subjective measures
Solution Approach 1:
The patent replaces subjective human evaluation (mechanical/system-based measurement) with automated computer vision algorithms that analyze visual features, audio characteristics, and text content of video shots. This substitution eliminates human bias while maintaining pre-release prediction capability, directly resolving the contradiction between early prediction availability and measurement objectivity
Solution Approach 2:
The system enables videos to predict their own success using their intrinsic raw assets (visual, audio, and text content) without requiring external subjective evaluation. The video content itself serves as the data source for prediction, making the system self-sufficient and objectively measurable while available before release
2Productivity
If subjective measures are used for video success prediction, then pre-release analytics can be generated, but the predictions are biased and less reliable
Solution Approach 1:
The patent segments the video into individual shots and further divides each shot into visual, audio, and text components. This granular segmentation allows for objective analysis of specific elements (e.g., shot duration, visual complexity, audio patterns) rather than relying on subjective overall evaluation, thereby improving prediction reliability while maintaining productivity
Solution Approach 2:
The system transforms subjective quality assessment into quantifiable parameters such as shot duration, visual feature vectors, audio spectral characteristics, and text sentiment scores. By changing the measurement parameters from subjective ratings to objective quantifiable metrics, the prediction becomes more reliable while remaining efficient to generate
Data Source
AI summary
Systems, methods, and computer program products to perform an operation comprising receiving a plurality of superclusters that includes at least one of a plurality of shot clusters, wherein each of the shot clusters includes at least one of a plurality of video shots, and wherein each video shot includes one or more video frames, and computing an expected value for a metric based on the plurality of superclusters.