Video Encoding Skip Coding Background Foreground Segmentation
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Solution Overview
Problem
Current video conferencing technologies face challenges in optimizing video communications, particularly in efficiently encoding and transmitting video data to ensure high-quality, low-bandwidth consumption, and effective processing of moving versus stationary image components.
Innovation Solution
The implementation of an advanced skip coding technique that uses change detection statistics to identify background and foreground image data, generates histograms to represent luminance variations, and determines which video elements to skip during encoding, thereby reducing computational resources and bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If advanced video processing techniques are used to improve video quality, then video quality is improved, but processing power consumption increases
Solution Approach 1:
The patent segments video processing into distinct components: background processing (identifying stationary elements) and foreground processing (identifying moving elements). By separating these processing tasks, the system can apply different encoding strategies to each segment, improving overall video quality while reducing total processing power consumption through targeted optimization of each segment.
Solution Approach 2:
The patent applies partial action by selectively processing only certain portions of video data. Specifically, it identifies stationary background elements and excludes them from full encoding processing, while only encoding moving foreground elements. This partial processing approach maintains video quality for important moving elements while significantly reducing overall processing power consumption.
2Manufacturing precision
If more video data is transmitted to maintain quality, then video quality is maintained, but bandwidth consumption increases
Solution Approach 1:
The patent extracts and separates background (stationary) elements from foreground (moving) elements in the video data. By taking out the stationary background components and excluding them from transmission, the system reduces bandwidth consumption significantly while maintaining video quality for the important moving foreground elements that contain the essential video information.
Solution Approach 2:
The patent applies local quality by differentiating processing and transmission priorities for different regions of the video. Moving foreground elements receive full encoding and transmission to maintain high quality, while stationary background elements are excluded or minimally processed. This localized quality approach optimizes bandwidth usage by focusing transmission resources on dynamically changing important elements.
3Productivity
If change detection statistics are used to identify background and foreground data, then processing efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-identifying and classifying video elements as stationary or moving during the initial processing stage using change detection statistics. This preliminary classification creates organized data structures that enable more efficient subsequent encoding and transmission operations, improving overall processing efficiency despite the initial computational investment in classification.
Data Source
AI summary
A method is provided in one example and includes receiving a video input from a camera element; using change detection statistics to identify background image data; using the background image data as a temporal reference to determine foreground image data of a particular video frame within the video input; using a selected foreground image for a background registration of a subsequent video frame; and providing at least a portion of the subsequent video frame to a next destination.