Feature Pyramid Flows for Video Frame Interpolation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video frame interpolation methods face inefficiencies in handling complex motion, occlusion, and high computational costs, particularly with deep neural networks (DNNs) that require large models, making them unsuitable for deployment on mobile devices.
Innovation Solution
A compression-driven network utilizing a multi-resolution warping module with a feature pyramid representation to assist in image synthesis, which includes a convolutional neural network with an encoder, decoder, and synthesis network to generate interpolated frames, and employs adaptive collaboration of flow models for parameter-efficient interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks with over 20 million parameters are used for video frame interpolation, then interpolation quality is improved, but model size and computational cost increase significantly
Solution Approach 1:
The patent segments the feature extraction process into a feature pyramid with multiple levels, where each level extracts features at different resolutions. This allows the model to process information at multiple scales without requiring a single massive network, thereby reducing overall model complexity while maintaining interpolation quality.
Solution Approach 2:
The patent introduces a multi-resolution feature pyramid that adds a dimensional aspect to feature representation. Instead of relying solely on increasing network depth/width, the solution exploits the resolution dimension to capture features at multiple scales, reducing the need for excessive parameters.
2Measurement precision
If deep neural networks with over 20 million parameters are used for video frame interpolation, then interpolation quality is improved, but training time and computational resources increase
Solution Approach 1:
By segmenting the network into modular components (encoder, feature pyramid, synthesis network), the patent enables more efficient training where each module can be optimized independently. This modular structure reduces the overall training time compared to training a monolithic large model.
Solution Approach 2:
The patent uses a feature pyramid that extracts features at multiple resolutions, allowing the model to focus computational effort on the most relevant feature scales rather than processing all details at maximum resolution, thereby reducing training time while maintaining quality.
3Measurement precision
If large DNN models are used for video frame interpolation, then interpolation performance is improved, but deployment on mobile devices becomes infeasible
Solution Approach 1:
The patent segments the feature extraction into a hierarchical feature pyramid with multiple levels. This segmentation allows the model to be more computationally efficient and suitable for mobile deployment by processing features at appropriate resolutions rather than requiring a single large network.
Solution Approach 2:
The patent changes the parameter representation by using multi-resolution features instead of relying on a large number of parameters in a single network. This parameter transformation enables the model to achieve good performance with a structure suitable for mobile devices.
4Device complexity
If pixel-level information only is used in optical flow techniques, then computational simplicity is maintained, but handling of complex occlusion and blur becomes inefficient
Solution Approach 1:
The patent adds a multi-resolution dimension to the feature representation through the feature pyramid. This allows the model to capture both fine details and broader contextual information, improving occlusion and blur handling while maintaining computational efficiency through the hierarchical structure.
Data Source
AI summary
Systems and methods for generating interpolated images are disclosed. In examples, image features are extracted from a first image and a second image; such image features may be warped using first and second plurality of parameters. A first candidate intermediate frame may be generated based on the warped first features and the warped second features. Multi-scale features associated with the image features extracted from the first image and the second image may be obtained and warped using the first and second plurality of parameters. A second candidate intermediate frame may be generated based on the warped first multi-scale features and the warped second multi-scale features. By blending the first candidate intermedia frame with the second candidate intermediate frame, an interpolated image may be generated.