Video Evaluation Apparatus for Automatic Highlight Detection
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Solution Overview
Problem
Users face challenges in simultaneously playing video games and selectively recording exciting or surprising footage for social media or sharing with friends, as they need to focus on reacting to the game.
Innovation Solution
An entertainment device and method for video evaluation that analyzes statistical metrics of image frames to identify exciting moments, using interframe delta sums and color channel analysis, and optionally incorporates audio energy metrics and perceptual hashes for scene cut detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user manually monitors and records video game footage, then the quality and selectivity of recorded content improves, but the user cannot simultaneously focus on reacting to the game
Solution Approach 1:
The system performs automatic video evaluation and highlight detection without requiring manual user intervention. The entertainment device autonomously analyzes video frames, computes metrics, and identifies exciting moments, allowing the user to simply play the game while the system handles the recording and selection process automatically
Solution Approach 2:
The manual mechanical process of monitoring and selecting footage is replaced with an automated computational system that uses algorithms to analyze video frames, compute metrics, and detect highlights automatically, eliminating the need for manual user operation
2Measurement precision
If comprehensive video analysis is performed to identify exciting moments, then the accuracy of highlight detection improves, but the computational cost increases
Solution Approach 1:
The video analysis is divided into multiple independent metric computations (motion metrics, color metrics, audio metrics) that can be processed separately and combined. This segmentation allows the system to analyze different aspects of the video independently, improving accuracy while managing computational load through modular processing
Solution Approach 2:
The system computes multiple metrics for each video frame (interframe delta, color channel deltas, audio energy) and combines them to determine excitement levels. By performing partial analysis on multiple parameters rather than exhaustive analysis of all possible features, the system achieves high detection accuracy with manageable computational cost
3Measurement precision
If multiple metrics are computed for each video frame to evaluate excitement, then the precision of video evaluation improves, but the device complexity increases
Solution Approach 1:
The evaluation system is segmented into distinct metric computation modules (motion analysis, color analysis, audio analysis) that each handle specific aspects of video evaluation. This modular segmentation improves measurement precision by dedicating specialized processing to each metric while managing device complexity through organized, independent modules
Solution Approach 2:
The entertainment device integrates multiple functions into a single system: video capture, frame analysis, metric computation, audio processing, and highlight detection. This multi-functionality allows the device to perform comprehensive video evaluation with improved precision while avoiding the complexity of multiple separate devices
Data Source
AI summary
A method of video evaluation for a video comprising successive video frames comprises the steps of, for successive video frame images: generating a perceptual hash of at least a first portion of a respective image, comparing the generated perceptual hash of a current video frame image with a corresponding perceptual hash of at least one preceding video frame image, and if a difference between the compared perceptual hashes exceeds a predetermined threshold, then identify the one preceding video frame as the boundary of a scene cut within the video.