API for Neural Network Frame Interpolation
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Solution Overview
Problem
High-quality video processing is hindered by significant memory and resource requirements, particularly in handling high-resolution videos with complex content, due to limitations in computing resources.
Innovation Solution
The use of 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 consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution video processing is performed to maintain video quality, then video processing quality is improved, but memory and computational resource requirements increase significantly
Solution Approach 1:
The patent creates a virtual copy of the video stream by generating interpolated frames that replicate intermediate moments between existing frames. This virtual copy allows quality enhancement without processing the entire original high-resolution video, reducing memory and computational resource requirements while maintaining processing quality.
Solution Approach 2:
The patent segments the video processing task by handling only the differences between frames rather than processing complete frames. By focusing on motion vectors and temporal differences, the system reduces the quantity of data to process while maintaining the quality of the enhanced video output.
2Productivity
If frame rate is increased to improve video smoothness and reduce motion artifacts, then video quality is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis of motion vectors and temporal differences between existing frames to predict and generate intermediate frames. This preliminary action allows the system to create interpolated frames efficiently without requiring extensive real-time processing, thus increasing frame rate while controlling processing time.
Solution Approach 2:
The patent replaces traditional mechanical frame-by-frame processing with a neural network-based interpolation system. This substitution enables faster frame rate generation by using learned patterns and mathematical models rather than exhaustive computational methods, reducing processing time while maintaining quality.
3Measurement precision
If complex video content with multiple subjects is processed to maintain accuracy, then processing accuracy is improved, but computational complexity and resource consumption increase
Solution Approach 1:
The patent applies local quality enhancement by processing only the regions of interest where motion and content changes occur. By focusing computational resources on areas with temporal differences and motion vectors rather than processing the entire frame uniformly, the system maintains processing accuracy for complex content while reducing overall computational complexity.
Solution Approach 2:
The patent introduces motion vectors and temporal difference maps as intermediary representations that simplify the processing of complex video content. These intermediaries capture essential information about subject movement and changes, allowing accurate processing of complex scenes with multiple subjects while reducing the computational complexity required compared to direct frame-by-frame analysis.
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 indicate support to use one or more neural networks to perform frame interpolation.