Neural Network Video Frame Blending for Resource Efficiency
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Solution Overview
Problem
High-resolution video processing requires significant computing resources and memory, making it challenging to achieve high-quality video with complex content and rapid processing needs due to the complexity of video information and limitations in computing resources.
Innovation Solution
A neural network-based system generates interpolated video frames by using blending factors to combine motion warped color frames, which are upscaled and downscaled to match the resolution of the video, allowing for efficient interpolation and enhancement of frame rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high resolution video processing is performed to maintain video quality, then video quality is improved, but computing resources and memory usage increase significantly
Solution Approach 1:
The video processing task is segmented into multiple frames, with intermediate frames generated independently between key frames. This allows the system to process only necessary portions of video data at high resolution while using lower resolution for other frames, reducing overall computing resource requirements while maintaining video quality.
Solution Approach 2:
Motion information and blending factors are pre-calculated for intermediate frames before actual video rendering. By preparing motion vectors and blending parameters in advance, the system reduces the computational burden during real-time video processing, enabling high quality output with reduced computing resources.
2Manufacturing precision
If complex video content is processed to maintain detail accuracy, then video quality is improved, but processing time increases
Solution Approach 1:
The system applies full-detail processing only to key frames and essential motion regions, while using simplified processing for intermediate frames. This partial action approach maintains detail accuracy where most needed while significantly reducing processing time for the overall video sequence.
Solution Approach 2:
Motion information from key frames is copied and applied to intermediate frames through motion estimation and compensation. This allows the system to maintain detail accuracy by reusing proven motion data rather than recalculating it, dramatically reducing processing time while preserving video quality.
3Productivity
If rapid video processing is performed to meet real-time requirements, then processing speed is improved, but video quality deteriorates
Solution Approach 1:
The system dynamically adjusts processing intensity based on frame type and motion complexity. Key frames receive full processing for high quality, while intermediate frames use accelerated processing with pre-calculated motion data. This dynamic approach maintains video quality where necessary while achieving rapid processing speeds overall.
Solution Approach 2:
Processing parameters such as resolution, motion estimation accuracy, and blending complexity are changed based on frame requirements. Intermediate frames use reduced parameters for fast processing, while key frames use full parameters for quality. This parameter adaptation enables rapid processing without significant quality deterioration.
4Manufacturing precision
If more computing resources are allocated to video processing, then video quality is improved, but resource efficiency worsens
Solution Approach 1:
High computing resources are allocated locally only to key frames and regions with significant motion or detail requirements. Intermediate frames and static regions use minimal processing resources. This local quality approach maintains video quality in critical areas while dramatically improving overall resource efficiency.
Solution Approach 2:
The same processing pipeline and algorithms are used for both key frames and intermediate frames, with resource allocation automatically adjusted based on frame type. This universal approach allows the system to maintain video quality while improving resource efficiency by avoiding separate high-cost processing paths.
Data Source
AI summary
Apparatuses, systems, and techniques to process image frames. In at least one embodiment, one or more intermediate video frames are generated between a first video frame and a second video frame. In at least one embodiment, the one or more intermediate video frames are generated based, at least in part, on depth information of one or more pixels of the first video frame or second video frame.