Video Super-Resolution Using Energy-Based GAN
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Solution Overview
Problem
Conventional video super-resolution methods rely on explicit motion estimation between frames, limiting their ability to handle unconstrained real-world video with rapid motion, blur, occlusions, and drastic appearance changes, resulting in visual flicker and color shifting.
Innovation Solution
A deep neural network, specifically an Energy-Based Generative Adversarial Network (EBGAN), is trained using individual still images to up-sample video sequences, producing higher resolution video with temporally stable and color-consistent frames, avoiding reliance on frame motion estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional super-resolution methods use explicit motion estimation between video frames, then reconstruction accuracy can be achieved under ideal conditions, but the method fails to handle unconstrained real-world video with rapid motion, blur, occlusions, and drastic appearance changes
Solution Approach 1:
The patent replaces the mechanical motion estimation system with a neural network-based system. Instead of explicitly estimating motion between frames using traditional algorithms, the neural network learns to directly map low-resolution frames to high-resolution frames, substituting the mechanical motion estimation process with a data-driven approach that handles unconstrained video content effectively
Solution Approach 2:
The patent changes the fundamental parameter of motion handling from explicit motion vectors to latent feature representations. By transforming the problem from motion-compensated reconstruction to direct pixel-space transformation through neural networks, the system adapts to handle rapid motion, blur, and occlusions without relying on accurate motion estimation
2Manufacturing precision
If conventional methods rely on motion estimation between frames, then theoretical reconstruction correctness can be achieved, but visual flicker and color shifting occur in practice
Solution Approach 1:
The patent applies preliminary training action by pre-training the neural network on large datasets of image pairs before deployment. This preliminary learning phase enables the network to capture temporal consistency patterns and color relationships, preventing flicker and color shifting during actual video processing without requiring complex runtime corrections
Solution Approach 2:
The patent implements feedback through the neural network's learned mappings that implicitly enforce temporal consistency. The network learns from training data to produce consistent outputs across frames, using the feedback embedded in the training process to maintain temporal stability and color consistency without explicit feedback mechanisms during inference
Data Source
AI summary
According to one implementation, a video processing system includes a computing platform having a hardware processor and a system memory storing a software code including an artificial neural network (ANN). The hardware processor is configured to execute the software code to receive a first video sequence having a first display resolution, and to produce a second video sequence based on the first video sequence using the ANN. The second video sequence has a second display resolution higher than the first display resolution. The ANN is configured to provide sequential frames of the second video sequence that are temporally stable and consistent in color to reduce visual flicker and color shifting in the second video sequence.