Motion Estimation for Remote Desktop via Window Tracking
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Solution Overview
Problem
The high computational complexity of motion estimation in remote desktop sharing leads to time-consuming encoding processes, which can increase bandwidth and storage costs due to the need for precise motion estimation for efficient encoding.
Innovation Solution
A system that includes a window analyzing component to identify window locations, sizes, and z-order, a tracking component to monitor window relocations and resizing, and a motion estimation component to estimate motion compensation units based on window information, reducing the complexity of motion estimation by leveraging window movement data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If precise motion estimation is performed to improve encoding quality and efficiency, then encoding quality and bit rate reduction are improved, but computational complexity and encoding time increase significantly
Solution Approach 1:
The patent segments the motion estimation process into two distinct stages: a coarse motion estimation stage that processes the entire frame to identify general motion patterns, and a refined motion estimation stage that focuses only on regions with detected motion. This segmentation reduces the overall computational complexity while maintaining encoding quality by avoiding exhaustive search in static regions.
Solution Approach 2:
The patent applies partial action by performing detailed refined motion estimation only in regions where motion is detected during the coarse estimation stage, rather than applying full motion estimation to the entire frame. This selective approach reduces computational complexity while maintaining precision where it matters most for encoding quality.
2Measurement precision
If full motion estimation is applied to all regions to ensure encoding quality, then encoding quality is maintained, but encoding time and processing speed increase
Solution Approach 1:
The patent divides the frame into motion-active regions and static regions based on coarse motion estimation results. Encoding processing is then segmented accordingly: detailed motion compensation is applied only to motion-active regions, while static regions use simplified encoding. This segmentation maintains encoding quality in dynamic areas while significantly improving overall encoding speed.
Solution Approach 2:
The patent applies partial action by performing computationally intensive motion compensation only in regions where motion is detected, rather than processing the entire frame with full motion estimation. This approach maintains encoding quality where needed while improving overall encoding throughput and speed.
Data Source
AI summary
This disclosure relates to systems and methods for estimation of motion in a frame as compared to a reference frame based upon knowledge of windows in the frame and reference frame.