Universal Neural Network File for Video Resolution Enhancement
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Solution Overview
Problem
Existing methods for improving image resolution, such as interpolation and machine learning techniques, face challenges in processing video data in real-time without high memory usage and are not universally applicable across different content types.
Innovation Solution
A resolution improvement system that includes a server generating a universal neural network file for improving image resolution, which is transmitted to a user device along with low-quality video data, allowing the device to perform computations and enhance the video quality using the neural network algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-quality video data is transmitted to improve resolution, then image quality is improved, but transmission speed decreases due to high data capacity
Solution Approach 1:
The server performs preliminary action by generating a universal neural network file in advance based on retained video data. This neural network file is then transmitted to the user device along with low-quality video data, enabling the device to perform resolution improvement locally without requiring high-quality data transmission from the server.
Solution Approach 2:
The universal neural network file acts as an intermediary that enables resolution improvement. Instead of transmitting high-quality video data directly, the system transmits low-quality video data combined with the neural network file, which serves as a mediator to restore high quality at the user device through computational application.
2Measurement precision
If resolution improvement is performed using traditional interpolation techniques, then processing speed is maintained, but image quality does not improve significantly
Solution Approach 1:
The patent replaces traditional mechanical interpolation techniques with an artificial neural network-based system. The universal neural network file, when applied through computational operations on the user device, substitutes for conventional interpolation methods, achieving both high image quality and acceptable processing speed through AI-driven resolution restoration.
3Adaptability or versatility
If a universal neural network file is generated and transmitted to enable resolution improvement, then adaptability across content types is improved, but data transmission requirements increase
Solution Approach 1:
The system generates a universal neural network file that serves multiple functions across different content types. This single neural network file can be applied to various video data types at the user device, providing universal adaptability for resolution improvement without requiring separate models for different content genres or formats.
Data Source
AI summary
A resolution improvement system includes a server, for performing, in response to a user device side request, a service for transmitting requested video data to a user device. A universal neural network file required for operation of an artificial neural network algorithm for improving the resolution of image information on the basis of the retained video data is generated, and the low-quality video data in which the generated universal neural network file and the resolution are changed to less than or equal to a preset level is transmitted to the user device. A user device performs a calculation operation on the basis of an artificial neural network algorithm that applies the received universal neural network file to the low quality video data received from the server, improving the resolution of the low quality video data according to the calculation operation, and playing back the video data with improved resolution.