Media Scene Segmentation for Context-Preserving Runtime Reduction
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Solution Overview
Problem
Viewers often find media content consumption time-consuming and may become bored or distracted, leading to the risk of missing important context due to fast-forwarding through less interesting parts.
Innovation Solution
A mechanism to automatically skip non-central scenes in media content using machine learning models, dividing content into segments and identifying removable scenes that do not affect the storyline, allowing for a shortened playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If viewers fast-forward through less interesting parts of media content, then viewing time is reduced, but the risk of missing important context increases
Solution Approach 1:
The media content is divided into multiple segments based on scene changes, character appearances, and narrative importance. The system analyzes each segment to determine whether it contains essential plot information or can be safely skipped, allowing selective viewing that preserves context while reducing overall viewing time.
Solution Approach 2:
The system extracts and identifies removable scenes that are non-essential to the storyline by analyzing scene content, character actions, and narrative flow. These extracted segments are then removed from the media content stream, creating a condensed version that maintains only the critical plot points and character development moments.
2Productivity
If machine learning models are used to identify removable scenes, then viewing can be expedited, but device complexity increases
Solution Approach 1:
The machine learning models are pre-trained on large datasets of media content to automatically recognize patterns of essential versus non-essential scenes. The system uses these pre-trained models to self-analyze media content without requiring manual intervention or complex real-time processing, reducing the operational complexity while maintaining high productivity.
Data Source
AI summary
Generally disclosed herein is a mechanism to expedite viewing of media content by automatically skipping scenes that are not central to the story or plot and not needed to fully understand and appreciate the media content. In detecting the scenes that can be skipped, one or more processors may divide media content into a plurality of segments and identify removable scenes within the segments that can be removed or skipped without affecting the storyline or viewing quality of the media content. The remaining segments are played consecutively, such as by removing the identified removable scenes or skipping past them, such that a shortened version of the media content is presented to the viewer.