Content-Aware Motion Estimation for Video Conferencing Power Optimization
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Solution Overview
Problem
Existing video conferencing technologies face challenges in optimizing power usage without compromising video quality, especially when dealing with varying levels of motion in video content, as they typically rely on resource-intensive features like Hierarchical Motion Estimation (HME) and Super HME, which consume significant GPU processing cycles and memory bandwidth.
Innovation Solution
A content-aware selective motion estimation system that adjusts the level of motion estimation based on visual data inputs, distinguishing between motion associated with the target user and the background, and considers power source availability to dynamically apply either base or enhanced motion estimation levels, thereby optimizing power usage while maintaining video quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If HME and Super HME are used to improve video quality in high-motion content, then video quality is improved, but power consumption increases due to GPU processing cycles and memory bandwidth usage
Solution Approach 1:
The system dynamically adjusts the motion estimation level (base or enhanced) based on real-time motion detection in video frames. When motion exceeds a threshold, enhanced motion estimation (HME/Super HME) is activated to improve quality; when motion is low, base motion estimation is used to conserve power. This dynamic adaptation resolves the contradiction by matching processing intensity to actual content requirements.
Solution Approach 2:
The system changes the motion estimation parameter (estimation level) based on detected motion characteristics. By analyzing motion magnitude in video frames and adjusting the motion estimation aggressiveness accordingly, the system optimizes the balance between video quality and power consumption, applying complex algorithms only when necessary.
2Manufacturing precision
If enhanced motion estimation is always applied to maintain video quality, then video quality is preserved, but GPU processing cycles and memory bandwidth are unnecessarily consumed in low-motion scenarios
Solution Approach 1:
The system applies partial motion estimation (base level) when full enhanced motion estimation is not needed. By detecting low-motion scenarios and applying only base motion estimation, the system avoids excessive processing while maintaining adequate video quality, thereby improving processing efficiency without sacrificing necessary quality.
Solution Approach 2:
The system uses feedback from motion detection analysis to control the motion estimation process. Motion magnitude calculated from frame differences feeds back into the decision logic that selects between base and enhanced motion estimation, creating a closed-loop system that optimizes processing efficiency based on actual content characteristics.
3Manufacturing precision
If motion estimation level is increased to handle active users with high motion, then video quality is improved, but power consumption increases on battery-powered devices
Solution Approach 1:
The system dynamically adapts motion estimation intensity based on detected motion levels and power source status. On battery-powered devices, the system monitors motion magnitude and adjusts accordingly: applying enhanced motion estimation only when motion exceeds thresholds (improving quality when necessary), and using base estimation during low-motion periods (extending battery life). This dynamic behavior resolves the contradiction between quality and battery duration.
Data Source
AI summary
Systems, apparatus, articles, and methods are described including operations for content aware selective adjusting of motion estimation.