Neural Networks for Video Encoding Artifact Detection
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Solution Overview
Problem
Existing video encoding schemes cause significant memory, time, and computing resource consumption due to video encoding artifacts, which are inefficient to identify and remove.
Innovation Solution
Utilizing neural networks to identify and remove video encoding artifacts by training them with various encoding settings, allowing for efficient detection and reduction of artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video encoding schemes are used, then video encoding artifacts are introduced, but removing these artifacts consumes significant memory, time, and computing resources
Solution Approach 1:
The patent replaces traditional mechanical/video processing methods with a neural network-based system. The neural network is trained to identify and remove encoding artifacts through learned patterns rather than conventional signal processing, substituting the mechanical removal process with an intelligent system that achieves better efficiency and resource utilization
Solution Approach 2:
The neural network is trained in advance on diverse video data with various encoding settings to pre-learn artifact patterns and removal strategies. This preliminary training enables the network to efficiently identify and remove artifacts during actual video processing without requiring complex real-time analysis, thereby improving processing efficiency
2Productivity
If neural networks are used to identify video encoding artifacts, then resource consumption is reduced, but training the neural network requires diverse encoding settings and data
Solution Approach 1:
The neural network is designed with a universal architecture that can handle multiple video encoding formats, codecs, and settings through a single unified model. This multi-functionality allows the network to process various video types without requiring separate specialized networks for each encoding scheme, thereby reducing overall system complexity despite the comprehensive training required
Solution Approach 2:
The training process systematically varies encoding parameters such as bitrate, resolution, and codec settings to expose the neural network to diverse artifact patterns. By changing these parameters during training, the network learns to generalize across different encoding conditions, reducing the need for complex specialized processing during deployment
Data Source
AI summary
Apparatuses, systems, and techniques to perform neural networks. In at least one embodiment, one or more neural networks are used to identify one or more video encoding artifacts based, at least in part, on a plurality of different video encoding settings.