Thumbnail Generation Using Blur and Image Distribution Analysis
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Solution Overview
Problem
The efficiency of reviewing, editing, and managing video content is hindered by the inability of existing key frames or thumbnails to effectively convey the subject matter, as they often appear unclear or unintuitive, and their effectiveness is further compromised by the features of the display device used.
Innovation Solution
A thumbnail generation system that ranks key frame candidates based on blur detection analysis, image distribution analysis, and display attributes to optimize the selection and generation of clear, recognizable, and intuitively identifiable thumbnails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional key frame selection methods are used, then video content can be reviewed and managed, but the thumbnails appear unclear or unintuitive and fail to effectively convey the subject matter
Solution Approach 1:
The patent changes multiple parameters including blur detection thresholds, image distribution metrics, and display attribute specifications to optimize thumbnail selection. By adjusting these parameters based on analysis results, the system achieves both clarity and effective subject matter conveyance in thumbnails.
Solution Approach 2:
The system implements feedback mechanisms by analyzing thumbnail candidates against multiple criteria (blur detection, image distribution, display attributes) and iteratively selecting the best candidates. This feedback loop ensures that selected thumbnails meet both clarity and information conveyance requirements.
2Adaptability or versatility
If multiple display device features are considered, then thumbnail effectiveness can be optimized for different devices, but the system complexity increases
Solution Approach 1:
The patent creates a universal thumbnail selection system that handles multiple display device types (mobile phones, tablets, desktops, televisions) through a single unified process. The system analyzes display attributes and adapts thumbnail selection to work effectively across all device types without requiring separate processing pipelines for each device.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameters based on display attributes. Rather than implementing complex device-specific processing, the system changes selection parameters according to the detected display type and characteristics, achieving adaptability through parameter modulation rather than structural complexity.
3Measurement precision
If manual thumbnail selection is performed, then clear and recognizable thumbnails can be achieved, but the time spent on video production and management increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically select and generate optimal thumbnails without human intervention. The automated analysis of blur, image distribution, and display attributes allows the system to make quality thumbnail selections independently, eliminating the time-consuming manual selection process while maintaining high thumbnail quality.
Solution Approach 2:
The system replaces the mechanical manual selection process with automated computational analysis. Instead of relying on human reviewers to manually examine and select frames, the patent uses computational algorithms to analyze video frames based on multiple criteria and automatically generate thumbnails, significantly reducing production time while maintaining or improving quality.
Data Source
AI summary
According to one implementation, a video processing system for performing thumbnail generation includes a computing platform having a hardware processor and a system memory storing a thumbnail generator software code. The hardware processor executes the thumbnail generator software code to receive a video file, and identify a plurality of shots in the video file, each of the plurality of shots including a plurality of frames of the video file. For each of the plurality of shots, the hardware processor further executes the thumbnail generator software code to filter the plurality of frames to obtain a plurality of key frame candidates, determine a ranking of the plurality of key frame candidates based in part on a blur detection analysis and an image distribution analysis of each of the plurality of key frame candidates, and generate a thumbnail based on the ranking.