Category-Adaptive Highlight Ranking for Video Recording Apparatus
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Solution Overview
Problem
Existing motion picture recording/reproducing technologies fail to provide optimal highlight reproduction for various types of motion picture data, as the definition of an important scene varies by category, leading to ineffective content grasping for busy users with overflowing data.
Innovation Solution
A motion picture recording/reproducing apparatus that includes a category acquiring unit, feature generating unit, ranking generating unit, and reproduction deciding unit, which adapts the importance level and scene length based on the category of motion picture data, using methods such as electronic program guides, sound recognition, and caption analysis to determine scene importance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single importance level criterion is used for all motion picture data, then the system is simple to operate, but it cannot provide suitable highlight reproduction for different categories of motion picture data
Solution Approach 1:
The system dynamically adjusts the importance level determination method based on the category of motion picture data. Different categories (drama, sports, news, etc.) trigger different ranking algorithms, allowing the system to adapt its behavior to the specific characteristics of each category rather than using a static, one-size-fits-all approach.
Solution Approach 2:
The system changes the parameters used for importance level determination according to the motion picture category. For example, sports categories may prioritize sound volume level and scene duration, while drama categories may use different criteria. This parameter adaptation allows the system to optimize highlight reproduction for each category without requiring a completely separate system for each type.
2Loss of information
If the system records all motion picture data, then complete data is stored for future reference, but users cannot efficiently find and view desired content among overwhelming quantities
Solution Approach 1:
The system performs preliminary analysis and ranking of motion picture data at the time of recording or before reproduction. By pre-calculating importance levels and organizing data according to category-specific criteria, the system prepares the data structure in advance, enabling users to quickly access desired content without performing time-consuming searches when they want to view the data.
Solution Approach 2:
The system extracts and highlights the most important scenes from complete motion picture data based on category-specific importance criteria. By separating and prioritizing key content while maintaining access to the complete dataset, the system allows users to efficiently view essential information without being overwhelmed by the full volume of recorded data.
3Productivity
If highlight reproduction is applied uniformly to all motion picture data, then the reproduction process is simple, but it fails to capture category-specific important scenes
Solution Approach 1:
The system applies different importance level determination methods to different categories of motion picture data. Each category receives a tailored analysis approach that considers its specific characteristics, ensuring that important scenes are identified with high precision according to category-specific criteria rather than using a uniform, imprecise method across all data types.
Data Source
AI summary
A motion picture recording/reproducing apparatus for embodying the technique includes, at least, a motion picture data input unit which inputs the motion picture data, a storage unit which stores the motion picture data, a recording unit which stores the motion picture data in the storage unit, a feature generating unit which generates a feature of the motion picture data, a ranking generating unit which provides ranking of scenes in the motion picture data according to their importance levels, a reproduction scene deciding unit which decides a reproduction image for each of the scenes in the motion picture data, and a category acquiring unit which acquires the category of the motion picture data. The ranking generating unit provides ranking of the scenes in the input motion picture data on the basis of the acquired category.