Adaptive Video Quality Assessment via Event Scoring
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Solution Overview
Problem
Current methods for assessing perceived video quality in adaptive media streaming, such as ITU-T Recommendations P.1201 and P.1202, do not account for quality adaptation events, making it difficult for broadcasters to ensure end-user satisfaction, especially in scenarios with varying network bandwidth and re-buffering events.
Innovation Solution
A method and system that determine the number of quality change events and the difference in quality levels over a predetermined period, generating a quality impact score to assess the perceived quality of adaptive media streaming, incorporating both audio and video signals, and using a computer program product with program instructions to analyze and calculate the quality impact score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ABR streaming is implemented to adapt video quality to varying network bandwidth, then the stream can be maintained during network congestion and user satisfaction can be improved, but it becomes difficult to accurately assess the overall perceived quality because quality adaptations occur over longer periods and re-buffering events impact the experience
Solution Approach 1:
The quality assessment is divided into multiple discrete quality change events detected over time. Each event represents a distinct adaptation point where quality parameters change, allowing the system to segment the continuous streaming experience into measurable units for accurate evaluation
Solution Approach 2:
The system proactively monitors and detects quality change events before they significantly impact user experience. By identifying adaptation events and re-buffering occurrences in advance, the system can assess their potential impact on overall quality and take appropriate measures to maintain service quality
2Ease of manufacture
If common video quality monitoring models (ITU-T P.1201/P.1202) are used to analyze packet header information and bit stream information, then basic quality metrics can be obtained, but they fail to capture quality adaptation events and re-buffering impacts that occur during ABR streaming
Solution Approach 1:
The system combines packet header analysis, bit stream information analysis, and quality change event detection into a unified monitoring framework. This integration allows the system to leverage existing monitoring capabilities while adding the specific functionality to detect and assess quality adaptation events and re-buffering impacts
Solution Approach 2:
The system introduces quality change events as an intermediary concept that bridges traditional quality metrics and user perceived quality. These events serve as markers that indicate when and how quality adaptations occur, allowing the system to translate technical streaming parameters into meaningful quality assessments that reflect actual user experience
Data Source
AI summary
A method for assessing perceived quality of adaptive media streaming includes receiving, by a device including a processor, a stream of adaptive media content. This stream includes both audio signals and video signals. A number of quality change events in the received stream for a predetermined period of time is determined. Also, a difference value between a highest quality level value detected in the received stream for the predetermined period of time and a lowest quality level value detected in the received stream for the predetermined period of time is determined. A quality impact score value is generated for the received stream based on the determined number of quality change events and based on the determined quality level difference value.