Content-Adaptive Video Sampling for Defect Detection
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Solution Overview
Problem
Current video quality monitoring systems face high computational, storage, and networking costs due to the need for extensive processing of video files using deep machine learning models for defect detection, making it unfeasible to scale effectively for large video catalogs and live events.
Innovation Solution
A content-adaptive video sampling system that uses fast complexity-measuring encoding, representative scene frame extraction, and defect-specific frame sampling based on heterogeneous video frame features, allowing for efficient extraction of a representative subset of video frames for defect detection, thereby reducing computational and storage requirements while maintaining high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep machine learning models are used for defect detection on all video frames, then defect detection accuracy is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The video frames are segmented into two categories: representative frames (selected based on complexity metrics and scene changes) and non-representative frames. Only representative frames undergo deep machine learning defect detection, while non-representative frames are processed through faster alternative methods or skipped entirely. This segmentation maintains detection accuracy for critical frames while reducing overall computational burden.
Solution Approach 2:
Different processing quality levels are applied to different frames based on their characteristics. Frames with high complexity metrics or significant scene changes receive full deep learning analysis (high quality processing), while simpler frames receive lighter processing (reduced quality but sufficient for detection). This local differentiation optimizes the balance between accuracy and computational cost.
2Reliability
If deep machine learning models are used for defect detection on all video frames, then defect detection capability is improved, but processing speed decreases
Solution Approach 1:
Before applying deep machine learning models, the system performs preliminary filtering using fast complexity-measuring encoding and scene frame extraction to identify representative frames. This preliminary action pre-selects only the frames that require intensive defect detection processing, ensuring that deep learning models are applied only where necessary, thus maintaining detection capability while improving overall processing speed.
Solution Approach 2:
Instead of applying full deep learning analysis to all frames (excessive action), the system applies partial processing to non-representative frames and full processing only to representative frames (partial action). This approach achieves sufficient defect detection capability for the entire video while significantly improving processing throughput by avoiding unnecessary intensive computation on frames that don't require it.
3Measurement precision
If all video frames are processed for defect detection, then detection thoroughness is improved, but storage and networking costs increase
Solution Approach 1:
The system extracts and processes only the essential subset of frames (representative frames) that contain the most information for defect detection. By taking out and focusing on these critical frames while excluding redundant frames, the system maintains detection thoroughness for important content while significantly reducing the quantity of data that needs to be stored and transmitted for processing.
Data Source
AI summary
Techniques for content-adaptive video sampling for automated video quality monitoring are described. As one example, a computer-implemented method includes receiving a request to train a machine learning model on a training video file comprising at least one labeled defect, performing an encode on the training video file to generate one or more compression features for each compressed frame of the training video file, training the machine learning model to identify a proper subset of candidate defect frames of the training video file based at least in part on the one or more compression features for each compressed frame of the training video file and the at least one labeled defect, receiving an inference request for an input video file, performing an encode on the input video file to generate one or more compression features for each compressed frame of the input video file, generating, by the machine learning model, a proper subset of candidate defect frames of the input video file based at least in part on the one or more compression features for each compressed frame of the input video file, and determining a defect in the input video file based at least in part on the proper subset of candidate defect frames of the input video file.