Video Quality Estimation Using Streaming Parameters
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Solution Overview
Problem
Existing methods for determining multimedia sequence quality, such as video quality, are computationally demanding, sensitive to frame synchronization, and require high-cost equipment, making real-time processing and cost-effective implementation challenging.
Innovation Solution
A method using an algorithm that derives quality metrics from video-streaming player and data transport parameters, eliminating the need for complex video image analysis and synchronization, allowing for real-time computation and reduced hardware requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video image analysis is used to estimate video quality, then measurement precision is improved, but device complexity and computational power requirements increase
Solution Approach 1:
The patent extracts only the essential parameters needed for video quality estimation from the full video signal. Instead of analyzing the entire video image content, it extracts key features such as motion vectors, block distortion metrics, and scene change indicators. This extraction approach maintains measurement precision while dramatically reducing computational complexity and device requirements.
Solution Approach 2:
The patent segments the video quality assessment into distinct analytical components that can be processed independently. It divides the video stream into blocks and analyzes specific features (motion, distortion, scene changes) separately rather than processing the entire frame simultaneously. This segmentation enables parallel processing and reduces the overall computational burden.
2Measurement precision
If full reference video analysis is used, then measurement precision is improved, but loss of time increases due to synchronization requirements
Solution Approach 1:
The patent removes the synchronization requirement entirely by extracting quality metrics that are inherently synchronization-independent. It uses metrics such as motion vector analysis and block distortion measurements that can be evaluated without requiring precise temporal alignment between reference and distorted frames, thereby eliminating time loss due to synchronization.
Solution Approach 2:
Instead of synchronizing frames and then analyzing them (traditional approach), the patent inverts the approach by analyzing local block features and motion characteristics that are inherently independent of global frame synchronization. This inversion eliminates the synchronization bottleneck while maintaining measurement accuracy.
3Measurement precision
If video image analysis algorithms are used, then measurement precision is improved, but productivity decreases due to high computational demands
Solution Approach 1:
The patent extracts only the critical quality-determining features from video frames, such as motion vectors, block distortion metrics, and scene change indicators. By extracting only these essential parameters rather than analyzing complete image data, it achieves acceptable measurement precision with significantly reduced computational requirements, enabling real-time processing.
Solution Approach 2:
The patent applies partial analysis by focusing only on the most influential video quality factors (motion, block distortion, scene changes) rather than performing exhaustive analysis of all image characteristics. This partial action approach maintains sufficient measurement precision while dramatically improving processing speed and productivity for real-time applications.
Data Source
AI summary
A method and a device utilizing an algorithm using measurement data derived from parameters related to a video-streaming player and/or parameters related to data transport is disclosed. The data are used as input data in a model designed to generate a value corresponding to the quality of the multimedia sequence, such as for example a MOS score.