Frame Size API for Neural Video Frame Interpolation
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Solution Overview
Problem
High-quality video processing is resource-intensive and complex, requiring significant computing power and memory, making efficient video enhancement difficult due to the large amount of information and varying pixel changes within the video content.
Innovation Solution
Utilizing neural networks to generate intermediate frames by spatial upsampling and optical flow analysis, followed by blending factors generated through a neural network to combine motion warped color frames, thereby increasing frame rate and resolution.
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 computational resources and processing time are significantly increased
Solution Approach 1:
The patent creates a virtual copy of the video stream by generating interpolated frames that replicate motion information between existing frames. This virtual copy allows the system to process and enhance video quality without requiring additional computational resources for direct high-resolution processing of the original video stream.
Solution Approach 2:
The system performs preliminary frame interpolation to create intermediate frames before final video processing. By pre-generating these interpolated frames using motion vectors from existing frames, the system prepares enhanced video content in advance, reducing real-time computational requirements while maintaining high quality.
2Productivity
If frame interpolation is performed to increase frame rate, then video smoothness is improved, but processing time and computational complexity are increased
Solution Approach 1:
The patent replaces traditional mechanical frame generation methods with neural network-based interpolation. Instead of using complex traditional video processing algorithms that require extensive computation, the system employs trained neural networks to efficiently generate interpolated frames, significantly reducing processing time while increasing frame rate.
Solution Approach 2:
The system changes the approach to frame interpolation by using neural network parameters and motion vectors to directly generate intermediate frames. This parameter-based approach using learned representations allows for faster computation compared to traditional pixel-level processing methods, enabling high frame rate output with reduced processing time.
3Productivity
If neural networks are used to generate interpolated frames, then processing efficiency is improved, but device complexity and computational requirements are increased
Solution Approach 1:
The patent implements a universal neural network framework that can process various video types and scenarios through a single multi-functional system. The neural network model is designed to handle different motion patterns, scene complexities, and video qualities, providing efficient frame interpolation across diverse applications without requiring separate specialized processing systems for each scenario.
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 frame size information using one or more neural networks.