Neural Video Upscaling for Low-Bandwidth Mobile Streaming
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video processing technologies face challenges in efficiently reducing bandwidth and processor consumption while maintaining video quality, particularly in mobile devices, leading to issues such as buffering, lag, and environmental impact due to high bandwidth and processing demands.
Innovation Solution
Implementing a machine learning-based video processing system that includes a downsampler and upsampler, utilizing neural networks to perform downsampling and upsampling independently of conventional codecs, with a kernel estimator to predict point spread functions and super resolution kernels for improved compression and decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional video processing and compression is used, then video quality can be maintained, but bandwidth and processor consumption increases leading to buffering and lag
Solution Approach 1:
The patent replaces conventional mechanical video processing systems with a machine learning-based system using neural networks. The neural network performs video frame processing, compression, and decompression, substituting traditional signal processing algorithms with learned models that optimize both quality and computational efficiency.
Solution Approach 2:
The system dynamically adjusts processing parameters based on input characteristics. The neural network learns optimal compression ratios, frame processing strategies, and resource allocation parameters by training on diverse video data, enabling adaptive parameter changes that balance quality and consumption.
2Reliability
If conventional video processing is used, then video quality can be maintained, but bandwidth consumption increases leading to environmental impact and cost
Solution Approach 1:
The system performs preliminary encoding and compression using the trained neural network before transmission. By pre-processing video frames through the learned model, the system optimizes compression efficiency and reduces the bandwidth required for transmission, thereby decreasing energy loss during data transfer.
3Loss of energy
If downsampling is performed to reduce bandwidth, then bandwidth consumption decreases, but video quality and detail are lost
Solution Approach 1:
The patent replaces traditional downsampling mechanical operations with a neural network-based processing system. The neural network learns to compress video data while preserving essential visual information and quality characteristics, substituting simple resolution reduction with intelligent learned compression that maintains video quality.
4Speed
If processing is performed in real-time, then video streaming responsiveness is improved, but processor consumption and complexity increase
Solution Approach 1:
The system performs preliminary training of the neural network model offline before deployment. By pre-training the model on diverse video data, the system reduces the computational complexity required for real-time processing, as the heavy learning work has already been completed beforehand.
Solution Approach 2:
The patent extracts the essential processing logic into a trained neural network model that can be deployed as a standalone component. This extraction separates the complex learning process from real-time execution, reducing the processing complexity required during actual video streaming while maintaining high speed performance.
Data Source
AI summary
Disclosed are systems, apparatuses, processes, and computer-readable media for upscaling video. An apparatus configured to process one or more frames (e.g., one or more video frames) comprises one or more processors coupled to the one or more memories. The one or more processors are configured to: obtain a first frame from an image sensor; downsample the first frame into a first downsampled frame based on a transfer function associated with the image sensor; and encode the first downsampled frame.