Automated Representative Image Selection Using Motion and Quality Metrics
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Solution Overview
Problem
Conventional technologies for selecting a poster image from a video clip are inefficient, often choosing the first frame which may be empty, blurry, or not representative, and rely on user subjective assessment, making the process time-consuming and unreliable.
Innovation Solution
A computer system executes a representative image selecting process that calculates motion vectors for each frame using motion estimation, orders frames by average motion vector magnitude, assesses pictorial quality through strength values, and selects the frame with the highest strength value as the representative image, ignoring frames with high motion and non-representative content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional technologies simply select the first frame from a video clip, then the selection process is extremely fast and simple, but the selected frame is often empty, blurry, or not representative of the video content
Solution Approach 1:
The patent replaces manual visual inspection with automated computer-based frame analysis. The system automatically evaluates multiple frames using computational algorithms that assess motion vectors, image quality metrics, and temporal characteristics to objectively determine the most representative frame, eliminating the need for human review while achieving higher accuracy than simple first-frame selection
Solution Approach 2:
The patent transforms the selection criteria from a single static parameter (first frame position) to multiple dynamic parameters including motion vector magnitude, image sharpness, contrast, and temporal positioning. By evaluating frames across these varied dimensions and weighting them appropriately, the system identifies the optimal representative frame that balances multiple quality attributes simultaneously
2Measurement precision
If users manually review each frame to select the representative image, then the selection can be subjective and time-consuming, but the process allows for human judgment and flexibility
Solution Approach 1:
The patent replaces manual visual inspection with automated computer-based frame analysis. The system automatically evaluates multiple frames using computational algorithms that assess motion vectors, image quality metrics, and temporal characteristics to objectively determine the most representative frame, eliminating the need for human review while achieving higher accuracy than simple first-frame selection
Solution Approach 2:
The system performs self-evaluation by automatically analyzing video frames and selecting the representative image without requiring human intervention. The automated algorithm independently processes the video content, calculates motion vectors, assesses image quality, and makes the selection decision, making the process both time-efficient and objectively accurate
3Measurement precision
If the system calculates motion vectors for all frames to determine representativeness, then the selection accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent divides the video clip into discrete frames and processes each frame independently through standardized computational steps. By segmenting the analysis into modular operations (motion vector calculation, image quality assessment, temporal positioning) that can be applied uniformly to each frame, the system manages computational complexity while maintaining high accuracy across the entire video sequence
Data Source
AI summary
A system determines a plurality of frames. The plurality of frames is a subset of a set of frames comprising at least a portion of a video clip. The frames are candidates to represent the set of frames. The system calculates a motion vector for each of the frames within the plurality of frames. The motion vector indicates an amount of motion in each of the frames with respect to at least one other frame from the plurality of frames. The system assesses a strength value for each of the frames. The strength value indicates an assessment of pictorial quality of each of the frames. The system selects a representative frame from the plurality of frames based on the motion vector and strength value. The representative frame indicates a most favorable representation of the plurality of frames.