Video Artifact Removal Model Selection for Mixed Compression Streams
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Solution Overview
Problem
Existing machine learning models for removing video compression artifacts are not well-suited for varied genres and levels of compression, making it difficult to adapt effectively to different video scenarios.
Innovation Solution
A system that selects and executes machine learning models for artifact removal and upscaling based on content and compression level, using a classifier to dynamically switch between models trained for specific scenarios, allowing for specialized processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine learning model is used for artifact removal, then the device complexity is reduced, but the adaptability to different video scenarios deteriorates
Solution Approach 1:
The patent segments the artifact removal task by dividing video content into different categories (e.g., natural scenes, screens, mixed content) and assigning specialized machine learning models to each category. This segmentation allows each model to be optimized for specific scenarios while the overall system maintains adaptability through category-based model selection.
Solution Approach 2:
The patent implements a dynamic model selection mechanism that automatically switches between different artifact removal models based on real-time analysis of video content characteristics. The system dynamically determines the appropriate model category for each video or video segment, enabling adaptability without requiring a single complex model to handle all scenarios.
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 patent introduces an intermediary classification mechanism that analyzes video content and determines the appropriate model category. This intermediary layer simplifies the complexity by providing a systematic approach to model selection based on content analysis, rather than requiring direct complex decisions about which of multiple specialized models to use.
Solution Approach 2:
The patent creates a universal framework that can handle multiple video scenarios through a standardized model selection process. The system uses a common classification mechanism that works across different video types, allowing the same infrastructure to support multiple specialized models without requiring separate complex selection logic for each scenario.
3Ease of manufacture
If machine learning models are trained on general datasets, then the ease of manufacture is improved, but the manufacturing precision of artifact removal deteriorates
Solution Approach 1:
The patent applies local quality by training different machine learning models on specialized datasets tailored to specific video content categories. Each model receives targeted training data relevant to its intended scenario (e.g., screen content models trained on screen recordings, natural scene models trained on video footage), resulting in superior artifact removal quality for each specific application while maintaining manageable training processes.
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.