Video Thumbnail Selection via Cross-Stream Frame Similarity
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Solution Overview
Problem
Existing methods for generating thumbnails for segmented video content are labor-intensive and often fail to represent the subject matter accurately, as they rely on manually selecting images from video frames that may not be informative about the content.
Innovation Solution
A method that automatically selects representative images for video segments by comparing features from a primary video stream with those of a secondary video stream containing similar content, using similarity metrics to identify the most relevant frames for use as thumbnails, thereby providing an informative graphical user interface for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of thumbnail images from video frames is used, then the process allows human judgment and selection, but it is labor-intensive and time-consuming
Solution Approach 1:
The system enables automatic thumbnail selection by having the video content itself provide the basis for selection. The method extracts features from video frames and uses similarity comparison with a secondary video stream to automatically identify representative frames, eliminating the need for manual human selection while maintaining accurate representation of the content.
2Productivity
If random or automatic selection of video frames is used for thumbnails, then the process is efficient and fast, but the thumbnails may not accurately represent the subject matter
Solution Approach 1:
The patent replaces manual mechanical selection with an automated computer-based system that uses feature extraction and similarity comparison algorithms. This substitution enables both high efficiency through automation and high precision through intelligent comparison of video features, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The method introduces a secondary video stream as an intermediary reference for comparison. By comparing features from the primary video stream against the secondary stream, the system identifies frames with highest similarity metrics, ensuring that selected thumbnails accurately represent the subject matter while maintaining automated efficiency.
3Measurement precision
If multiple video streams are compared using feature extraction and similarity metrics, then accurate representative frames can be identified, but the computational complexity increases
Solution Approach 1:
The method extracts only the essential features from video frames that are necessary for similarity comparison, rather than processing entire frames or all possible attributes. This extraction approach maintains high accuracy in identifying representative frames while reducing computational complexity by focusing only on the most relevant features for content representation.
Data Source
AI summary
A method of identifying a representative image of a video stream is provided. Similarity between video frames of a primary video stream relative to video frames of a different secondary video stream having similar content is evaluated and a video frame from the primary video stream having a greatest extent of similarity relative to a video frame of the secondary video stream is identified. The identified video frame is selected as an image representative of the primary video stream and may be used as an informative thumbnail image for the primary video stream. A video processing electronic device and at least one non-transitory computer readable storage medium having computer program instructions stored thereon for performing the method are also provided.