Machine-Learned Genre Classification for Adaptive Video Compression
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Solution Overview
Problem
Existing video compression techniques require numerous parameters for tuning, which can have negative effects on different types of content, and users often lack the knowledge to make informed choices, leading to suboptimal compression results.
Innovation Solution
A system utilizing a machine learning model for genre classification to determine the appropriate encoding parameters for video frames, enabling dynamic and content-adaptive video compression by identifying genres through features like noise, motion vectors, and pixel intensity, and adjusting encoding parameters accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep video compression techniques are used to reduce bitrate, then compression efficiency is improved, but the system requires many parameters for tuning which increases device complexity and reduces ease of operation
Solution Approach 1:
The system automatically determines encoding parameters by analyzing video content features (noise, motion vectors, pixel intensity) and classifying genres, eliminating the need for manual parameter tuning. The machine learning model performs self-service parameter selection based on the video content characteristics, resolving the contradiction between compression efficiency and operational simplicity.
Solution Approach 2:
The system dynamically changes encoding parameters based on the classified video genre. Different genres trigger different parameter sets (e.g., higher quality for documentaries, lower quality for entertainment), allowing the system to optimize compression efficiency for each content type without requiring manual intervention to adjust parameters.
2Productivity
If a single encoding parameter is used to improve quality or reduce bitrate, then compression performance is improved for one content type, but it has negative effects on other content types due to lack of adaptability
Solution Approach 1:
The system applies different encoding qualities to different content regions and types. By classifying video genres and analyzing local content characteristics, the system adjusts encoding parameters locally for each genre (e.g., preserving quality for educational content while allowing higher compression for entertainment content), thereby optimizing performance for each specific content type rather than using a uniform approach.
Solution Approach 2:
The encoding parameters are made dynamic and adaptive rather than static. The system continuously analyzes video content, classifies genres, and adjusts parameters in real-time based on the detected content characteristics. This dynamic adaptation allows the system to optimize compression performance for each content type without compromising overall versatility.
3Ease of operation
If parameter selection is left to users through configuration input, then user control is improved, but most users cannot provide beneficial settings due to lack of knowledge about video sequence relationships and parameters
Solution Approach 1:
The system performs self-service by automatically analyzing video content features and selecting appropriate encoding parameters without requiring user knowledge. The machine learning model independently determines the best parameters based on genre classification and content characteristics, eliminating the information loss that occurs when users manually configure settings without proper expertise.
4Adaptability or versatility
If genre classification is performed on each frame to enable dynamic encoding, then content adaptability is improved, but processing time increases which reduces productivity
Solution Approach 1:
The system performs preliminary genre classification on video frames before encoding, using pre-trained machine learning models that quickly identify content types. By preparing the classification results in advance and using efficient feature extraction (noise, motion vectors, pixel intensity), the system minimizes processing time while maintaining high content adaptability for dynamic encoding.
Data Source
AI summary
Systems and methods herein are for at least one execution unit that can perform an inference using a machine learning (ML) model and that is coupled to a video encoder, where the ML model can determine a genre associated with received frames of a media stream based in part on using ML model features associated with different genres, where the video encoder can encode the media stream based in part on the determined genre.