Neural Network Frame Interpolation API for Video Processing
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Solution Overview
Problem
High-quality video processing is hindered by significant memory and resource requirements, particularly for high-resolution videos with complex content, due to limitations in computing resources and the complexity of processing multiple subjects with varying motions.
Innovation Solution
A system utilizing neural networks to generate interpolated video frames by blending motion warped color frames based on blending factors, which are upscaled to match the resolution of the original frames, allowing for efficient frame rate increase without excessive resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution video processing is performed to maintain high quality, then video quality is improved, but computing resources and processing time are significantly increased
Solution Approach 1:
The patent segments the video processing task by separating original frames from generated intermediate frames. Instead of processing all frames at full resolution, the system processes only the original frames through neural networks to generate intermediate frames, reducing the computational burden while maintaining quality.
Solution Approach 2:
The patent creates synthetic intermediate frames by copying and blending information from original frames. The neural network generates interpolated frames that replicate the motion and content between original frames, allowing high-quality output without processing every frame through heavy computation.
2Productivity
If frame rate is increased by generating intermediate frames, then video smoothness is improved, but processing time and computational load are increased
Solution Approach 1:
The patent performs preliminary processing by pre-computing motion vectors and blending factors from original frames before generating intermediate frames. This preliminary action allows the neural network to efficiently generate intermediate frames without requiring real-time analysis of all frame combinations, reducing overall processing time.
Solution Approach 2:
The patent replaces traditional mechanical frame-by-frame processing with neural network-based generation. Instead of processing each intermediate frame individually through complex algorithms, the system uses neural networks to directly generate intermediate frames from original frames, significantly reducing computational time.
3Manufacturing precision
If complex video content with multiple moving subjects is processed, then video fidelity is improved, but processing complexity and resource requirements are increased
Solution Approach 1:
The patent creates a universal neural network model that can handle various types of video content and motion patterns through a single integrated system. The model learns general motion representation and blending techniques that work across different video scenarios, eliminating the need for separate processing complexes for different content types.
Solution Approach 2:
The patent changes the processing parameters by using neural network-based motion estimation and blending factors instead of traditional fixed algorithms. This allows the system to adapt to complex video content dynamically through learned parameters rather than rigid processing rules, maintaining fidelity while reducing processing complexity.
Data Source
AI summary
Apparatuses, systems, and techniques to process image frames. In at least one embodiment, an application programming interface (API) is performed to cause frame interpolation to be performed using one or more neural networks.