Motion-Adapted Video Quality Metric for Congested Networks
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Solution Overview
Problem
Existing video conferencing systems struggle to effectively measure and manage video quality metrics, particularly in congested networks, leading to packet loss, transcoding errors, and decoding noise, which negatively impact the quality of experience (QoE) for users.
Innovation Solution
A computer-implemented method and system for determining a spatial-temporal video quality metric (VQM) that assesses video streams by identifying repeated frames, using a motion-adapted video quality assessment mechanism to generate an alarm when the quality falls below a threshold, allowing for real-time adaptation of transmission parameters to ensure minimum QoE.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video streams are transmitted over congested networks, then network coverage and reachability are improved, but packet loss and video quality degradation occur
Solution Approach 1:
The system performs preliminary actions by identifying repeated video frames before transmission and using them to compensate for packet loss. The motion-adapted VQM mechanism is pre-configured to detect quality degradation and trigger frame repetition strategies proactively rather than reactively, maintaining video quality even when packets are lost during transmission over congested networks.
Solution Approach 2:
The system changes transmission parameters dynamically by adjusting the frequency of frame repetition based on detected packet loss patterns and motion content. When motion is detected, the system reduces frame repetition to save bandwidth; when motion is minimal or packet loss is high, it increases frame repetition. This adaptive parameter adjustment resolves the contradiction between maintaining quality and managing network bandwidth.
2Reliability
If frame repetition is used to compensate for packet loss, then video quality is maintained, but network bandwidth consumption increases
Solution Approach 1:
The system implements dynamic frame repetition strategies where the repetition rate is adjusted in real-time based on motion detection and packet loss patterns. The motion-adapted VQM mechanism continuously monitors video content and modifies the frequency of repeated frames accordingly. This dynamic approach maintains video quality by repeating frames only when necessary (low motion periods) rather than uniformly across all frames, thereby reducing overall bandwidth consumption while preserving quality during critical moments.
Solution Approach 2:
The system applies frame repetition selectively based on local video content characteristics. Instead of uniformly repeating all frames, it identifies regions of low motion or static content where frame repetition is less noticeable and applies repetition primarily in those areas. This localized quality maintenance approach preserves perceived video quality while minimizing the total number of repeated frames transmitted, thus reducing bandwidth consumption.
3Measurement precision
If motion-adapted VQM is implemented to assess video quality, then quality measurement precision is improved, but computational complexity increases
Solution Approach 1:
The motion-adapted VQM system segments the video stream into distinct motion regions and static regions, applying different quality assessment strategies to each segment. By dividing the video content into manageable segments based on motion characteristics, the system can perform precise quality measurements on dynamic regions while using simpler assessments for static regions. This segmentation reduces overall computational complexity while maintaining high measurement precision where it matters most.
Solution Approach 2:
The system performs partial quality assessment by focusing computational resources on key frames and regions of interest rather than analyzing every single frame in full detail. The motion-adapted VQM mechanism identifies critical moments (such as scene changes or high-motion sequences) and applies comprehensive quality assessment only at those points, while using lighter-weight metrics for intermediate frames. This partial action approach maintains adequate measurement precision for quality monitoring while significantly reducing computational complexity and processing overhead.
Data Source
AI summary
Various embodiments comprise systems, methods, and apparatus for processing a received video stream according to an embodiment comprises: identifying a number of repeated video frames within a sequence of N video frames within the video stream; determining, using a video frame quality assessment mechanism adapted to use repeated frames information, a motion adapted video quality metric (VQM) of the sequence of N video frames; and generating an alarm in response to the motion adapted VQM being less than a threshold level.