Motion Vector Frame Generation for Efficient Video Interpolation
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Solution Overview
Problem
Existing frame-generating techniques for frame interpolation in image enhancement using neural networks are limited in their ability to efficiently generate additional frames, particularly in computer graphics videos, without separately generating motion vectors.
Innovation Solution
A method and apparatus that utilize a frame generation model incorporating both image-based and motion-based models to perform encoding and decoding operations, leveraging motion vectors and optical flow features to generate intermediate frames by performing encoding and decoding operations on image and motion information, and using a combination of optical flow and motion vectors to warp and merge frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing frame-generating techniques using neural networks are used for frame interpolation, then additional frames can be generated between original frames, but the ability to efficiently generate frames in computer graphics videos is limited without separate motion vector generation
Solution Approach 1:
The patent combines the image-based neural network model and motion-based model into a unified frame generation model. The motion-based model generates motion vectors that are integrated with the image features from the neural network, eliminating the need for separate motion vector generation processes and improving overall efficiency while reducing process complexity.
Solution Approach 2:
The frame generation model is designed to perform multiple functions: it processes image features through the neural network, generates motion vectors through the motion-based model, and synthesizes intermediate frames by combining both. This multi-functional approach allows the system to handle both image enhancement and motion estimation in a single integrated process.
2Reliability
If neural networks are used to generate additional frames, then frame rate can be increased, but the quality and efficiency of frame interpolation is limited without incorporating motion vectors
Solution Approach 1:
The patent uses a composite approach by combining two different models: the image-based neural network model that captures visual features and the motion-based model that provides motion vectors. This composite model leverages the strengths of both approaches, where the neural network processes image data and the motion-based model provides motion information, resulting in higher quality frame interpolation without sacrificing processing efficiency.
3Measurement precision
If separate motion vector generation is performed for frame interpolation, then motion accuracy can be improved, but the overall process complexity and processing time increase
Solution Approach 1:
The motion-based model within the unified frame generation model performs preliminary motion estimation by generating motion vectors that are directly integrated with the image processing pipeline. This preliminary action of motion vector generation is embedded within the overall frame generation process, allowing accurate motion estimation to be achieved without adding separate processing steps or increasing overall process complexity.
Data Source
AI summary
Provided is a method and apparatus for generating an image frame using a motion vector. The method includes performing a first encoding operation based on a first image frame at a first time point and a second image frame at a second time point to generate a first encoding feature, performing a first decoding operation based on the first encoding feature to generate a first optical flow feature between the first time point and a third time point and a second optical flow feature between the second time point and the third time point, and generating a third image frame at the third time point based on the first optical flow feature, the second optical flow feature, and a motion vector corresponding to motion between the first image frame and the second image frame.