Compression-Aware Model Selection for Video Artifact Removal
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Solution Overview
Problem
Existing machine learning models for removing video compression artifacts are not well-suited for varied genres and compression levels, making it difficult to adapt effectively to different video scenarios, leading to suboptimal quality degradation.
Innovation Solution
A system that selects and executes machine learning models for artifact removal and upscaling based on content and compression level analysis, allowing for specialized models trained on specific scenarios, enabling adaptive and high-quality artifact removal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning model is used for artifact removal, then the system complexity is reduced, but the adaptability to different video scenarios deteriorates
Solution Approach 1:
The system divides the artifact removal task into multiple specialized machine learning models, each trained for specific video scenarios (e.g., different compression levels, content types). A classifier segments the input video and routes it to the appropriate model, achieving high adaptability without requiring a single complex universal model.
Solution Approach 2:
The patent introduces a classifier as an intermediary component that analyzes video characteristics and selects the most suitable pre-trained model for the given scenario. This mediator enables dynamic adaptation to different video scenarios while keeping the individual models relatively simple and specialized.
2Adaptability or versatility
If multiple specialized machine learning models are used for different video scenarios, then the adaptability to different video scenarios is improved, but the device complexity increases
Solution Approach 1:
The system divides the artifact removal task into multiple specialized machine learning models, each trained for specific video scenarios (e.g., different compression levels, content types). A classifier segments the input video and routes it to the appropriate model, achieving high adaptability without requiring a single complex universal model.
Solution Approach 2:
The patent creates a universal artifact removal system that handles multiple video scenarios through a combination of a general classifier and specialized models. The classifier provides multi-functionality by recognizing different video types and directing them to appropriate models, making the overall system adaptable to various scenarios while maintaining manageable complexity.
3Loss of time
If machine learning models are trained on generalized data, then the training efficiency is improved, but the manufacturing precision of artifact removal deteriorates
Solution Approach 1:
Instead of training a single model on generalized data, the patent trains multiple models on specialized datasets tailored to specific video scenarios (e.g., different compression levels, content genres). Each model achieves high precision for its specific domain by focusing on local characteristics rather than attempting to cover all scenarios equally.
Solution Approach 2:
The system performs preliminary classification of video scenarios before applying artifact removal. By pre-training models on scenario-specific data and using a classifier to identify the scenario type first, the system ensures that the appropriate specialized model is selected, thereby achieving high artifact removal quality without requiring all models to be trained on all types of data.
Data Source
AI summary
Systems and techniques are generally described for selecting a machine learning model for compression artifact removal and resolution upscaling of video streaming data. In various examples, a system or method receives a stream of video data, determines a category of the stream of video data based at least partially upon a compression level of the stream of video data, selects weights for a machine learning model based upon the category, and executes the machine learning model with the selected weights to remove compression artifacts in the stream of video data and upscale a resolution of the stream of video data.