Video Frame Region Prediction for Selective Rendering
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Solution Overview
Problem
Existing video post-processing methods on data processing devices are computationally expensive, leading to video frame-drops that detract from user experience, as they apply enhancements to entire video frames rather than focusing on the portions where the user is likely to focus.
Innovation Solution
A method and system that predict and enhance only the portions of a video frame where the user is likely to focus, using a processor to analyze motion vectors and audio content to determine high-activity areas, applying post-processing algorithms selectively to these areas while maintaining lower quality for peripheral vision regions, thereby reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If post-processing algorithms are applied to entire video frames, then video quality is enhanced, but computational cost increases and frame-drops occur
Solution Approach 1:
The video frame is segmented into multiple regions based on user focus probability. Post-processing algorithms are selectively applied only to high-probability regions (e.g., region of interest) rather than the entire frame. This segmentation allows quality enhancement where needed while maintaining speed by skipping low-priority areas.
Solution Approach 2:
Different quality levels are applied to different regions of the video frame based on predicted user focus. High-quality post-processing is applied to regions where users are likely to focus, while lower-quality rendering is used for peripheral or less important regions. This creates locally optimized quality distribution.
2Manufacturing precision
If post-processing algorithms are applied to entire video frames, then video quality is enhanced, but processing power is consumed
Solution Approach 1:
The frame is divided into processed regions (high user focus probability) and unprocessed regions (low user focus probability). Post-processing algorithms are executed only on segmented high-priority regions, significantly reducing the total processing power required while maintaining perceived video quality.
Solution Approach 2:
Instead of applying post-processing to the entire frame (excessive action), the system applies processing only to the necessary portions (partial action) where users are predicted to focus. This partial processing approach consumes less energy while achieving the same user-perceived quality improvement.
3Manufacturing precision
If post-processing algorithms are applied to entire video frames, then video quality is enhanced, but frame-drops occur detrimental to user experience
Solution Approach 1:
By segmenting the frame and processing only critical regions, the system reduces overall processing time and computational load. This prevents frame-drops and maintains consistent frame rendering, improving reliability while still enhancing quality in important areas.
Solution Approach 2:
Applying post-processing partially (only to high-probability regions) rather than excessively (to entire frames) reduces processing time enough to prevent frame-drops, maintaining both quality enhancement and frame rendering consistency.
Data Source
AI summary
A method includes predicting, through a processor of a data processing device communicatively coupled to a memory, a portion of a video frame on which a user of the data processing device is likely to focus on during rendering thereof on a display unit associated with the data processing device. The video frame is part of decoded video data. The method also includes rendering, through the processor, the portion of the video frame on the display unit at an enhanced level compared to other portions thereof following the prediction of the portion of the video frame.