Emotion Scene Extraction for Moving Image Reproduction
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Solution Overview
Problem
Existing technologies do not effectively utilize emotion data indicating user emotions for each scene of moving image content, limiting its application in reproduction and editing processes.
Innovation Solution
An information processing apparatus that extracts emotion representative scenes based on emotion metadata, allowing for the reproduction, editing, and display of user emotions, using units such as extraction, reproduction, and display control to identify and isolate key emotional scenes within moving image content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If emotion data is stored for each scene of moving image content, then user emotion information is preserved in detail, but data storage requirements and processing complexity increase
Solution Approach 1:
The patent extracts only the essential emotion representative scenes from the entire moving image content based on emotion metadata. The extraction unit identifies and extracts specific scenes where user emotions are represented, rather than processing all scenes. This reduces data processing complexity while preserving important emotion information.
Solution Approach 2:
The patent segments the moving image content into discrete scenes with associated emotion metadata. Each scene is analyzed independently for emotion characteristics, allowing selective extraction of emotion representative scenes. This segmentation enables efficient processing by breaking down the large dataset into manageable scene-level units.
2Ease of operation
If all scenes of moving image content are reproduced, then complete content is displayed, but user cannot efficiently locate key emotional moments
Solution Approach 1:
The patent performs preliminary extraction of emotion representative scenes before reproduction. The extraction unit pre-identifies and marks scenes with significant user emotions based on emotion metadata. During reproduction, the reproduction control unit can directly access these pre-identified scenes, eliminating the need for users to manually search through entire content to find emotional moments.
Solution Approach 2:
The patent introduces emotion metadata as an intermediary layer between the moving image content and the user. This metadata acts as an index or guide that enables efficient location of emotion representative scenes without requiring users to view or analyze the actual video content manually.
3Productivity
If emotion representative scenes are extracted and reproduced selectively, then reproduction efficiency is improved, but some content may be missed
Solution Approach 1:
The patent applies different quality levels to different parts of the content based on their emotional significance. Emotion representative scenes are extracted and reproduced with higher priority and detail, while non-emotional scenes are either omitted or reproduced with lower priority. This local quality differentiation optimizes reproduction efficiency by focusing resources on emotionally significant content.
Solution Approach 2:
The patent changes the reproduction parameters (such as reproduction speed, resolution, or priority) for emotion representative scenes compared to regular scenes. Extracted emotion scenes can be reproduced with enhanced parameters to emphasize their importance, while maintaining overall content coherence through controlled parameter variations.
Data Source
AI summary
Information processing that effectively uses emotion data indicating a user emotion for each scene of a moving image content is disclosed. In one example, an extraction unit extracts an emotion representative scene on the basis of emotion metadata having user emotion information for each scene of the moving image content. Reproduction of a part of the moving image content and editing of extracting a part of the moving image content can be effectively performed on the basis of the extracted emotion representative scene. For example, the extraction unit extracts the emotion representative scene on the basis of a type of the user emotion or a degree of the user emotion.