Reference-less Video Quality Assessment for Surveillance Systems
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Solution Overview
Problem
Surveillance systems face challenges in generating high-quality videos due to limitations in memory, processing power, and bandwidth, and manual evaluation of video quality is impractical given the vast amount of data generated, especially since there is no reference video for ideal quality.
Innovation Solution
A computer system uses a reference-less machine learning model to assess video quality by extracting feature vectors from captured videos, determining quality metrics, and adjusting camera parameters to improve video quality, enabling batch and near-real-time analysis of content generated by electronic devices like security cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of video quality is performed, then quality assessment accuracy is improved, but processing time and labor cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical evaluation with an automated machine learning model that processes video content. The model extracts features from video frames and predicts quality metrics automatically, eliminating the need for human reviewers while maintaining assessment accuracy through sophisticated algorithms trained on large datasets of video quality annotations.
Solution Approach 2:
The system performs self-assessment of video quality through the machine learning model that autonomously analyzes video content and generates quality metrics without external intervention. The model serves itself by automatically processing uploaded videos, extracting relevant features, and producing quality assessments that can trigger further actions like re-encoding or rejection.
2Measurement precision
If reference-based quality assessment is used, then quality measurement accuracy is improved, but system complexity and storage requirements increase
Solution Approach 1:
The patent extracts essential quality indicators from complex video content through feature extraction mechanisms. Instead of comparing entire video files against reference videos, the system identifies and analyzes key features such as motion patterns, compression artifacts, and structural elements that most strongly correlate with perceived quality, thereby simplifying the assessment process while maintaining accuracy.
Solution Approach 2:
The machine learning model acts as an intermediary between the video content and quality assessment. Rather than directly comparing videos, the model processes video features through learned transformations and predictions, serving as a mediator that translates complex visual data into meaningful quality metrics without requiring direct reference video comparisons.
3Manufacturing precision
If high-resolution video is captured and processed, then video quality is improved, but memory consumption and processing power requirements increase
Solution Approach 1:
The system extracts only the essential features needed for quality assessment from high-resolution video content, rather than processing and storing all video data. By identifying and analyzing only the most relevant visual characteristics that impact perceived quality, the system maintains high-quality evaluation capabilities while significantly reducing memory and computational requirements.
Solution Approach 2:
The patent applies local quality assessment by focusing analysis on specific regions or features of video content that most strongly influence overall quality perception. Instead of uniformly processing entire high-resolution frames, the system identifies and analyzes critical local areas such as motion regions, edge structures, or artifact-prone zones, thereby maintaining assessment accuracy with reduced computational overhead.
4Productivity
If batch video analysis is performed, then processing efficiency is improved, but time delay for individual video feedback increases
Solution Approach 1:
The system implements periodic batch processing where videos are analyzed in scheduled groups rather than continuously one-by-one. This periodic approach allows the system to accumulate multiple videos for efficient batch processing while still providing timely feedback within each processing cycle, balancing overall throughput with individual response times through regular processing intervals.
Solution Approach 2:
The patent introduces dynamic batch sizing and processing scheduling that adapts to system load and video characteristics. Batch parameters such as group size, processing intervals, and priority levels are dynamically adjusted based on incoming video streams, system resources, and quality requirements, allowing the system to optimize between batch processing efficiency and individual video feedback timing in real-time.
Data Source
AI summary
Introduced here are technologies for examining content generated by electronic devices in real time to optimize the quality of the content. The content may be examined in batches to address some of the drawbacks of real-time analysis. For instance, a series of videos may be examined to collect data on how well security system(s) that are presently employed are working. Each security system can include one or more electronic devices, such as cameras or microphones, and parameters of the electronic devices can be altered to improve the quality of content generated by the electronic devices.