Content-Adaptive Encoder Configuration for Dynamic Media Streams
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Solution Overview
Problem
Traditional media content encoding methods rely on presets that fail to optimize performance-compression tradeoffs for specific content types, especially in dynamic environments like real-time broadcast video encoding, where content classes change frequently, leading to suboptimal encoder performance and compression efficiency.
Innovation Solution
A content-adaptive encoder configuration method that uses machine learning to classify content and dynamically select toolsets based on both content class and desired performance level, minimizing performance overhead and enabling precise control over encoder performance across various content classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If presets are applied to reduce computing power during encoding, then encoding performance is improved, but compression efficiency deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static presets to dynamic, content-adaptive encoder configuration. The system continuously analyzes content characteristics and adjusts encoder parameters in real-time, allowing the encoding tools to adapt their behavior based on the specific content being processed, thereby optimizing the performance-compression tradeoff dynamically rather than using fixed preset values
Solution Approach 2:
The patent changes parameters by modifying encoder tool selections and restrictions based on detected content characteristics. Instead of using fixed preset parameters, the system adjusts encoding parameters such as tool restrictions, mode decisions, and quality settings according to the actual content analysis, enabling precise control over the performance-compression tradeoff for each specific content type
2Reliability
If manual selection of tune modes is required, then encoder configuration can be optimized for specific content types, but ease of operation deteriorates
Solution Approach 1:
The patent applies self-service by enabling the encoding system to automatically detect content characteristics and select appropriate encoder tools without user intervention. The content analyzer and toolset configurator work autonomously to identify content types and configure encoding parameters, eliminating the need for users to manually select tune modes while maintaining optimized encoding performance
Solution Approach 2:
The patent implements feedback by using content analysis results to automatically adjust encoder configuration. The system continuously monitors content characteristics and uses this feedback to dynamically select and adjust encoding tools, creating a closed-loop system that adapts to content changes without requiring manual user input or reconfiguration
3Device complexity
If traditional preset approach is used, then device complexity is reduced, but adaptability to dynamic content classes deteriorates
Solution Approach 1:
The patent applies preliminary action by performing content analysis and classification before encoding begins. The system pre-analyzes content characteristics, detects content classes, and selects appropriate encoder tools in advance, allowing the encoder to be properly configured before actual encoding starts. This preliminary configuration enables the system to handle dynamic content changes effectively
Solution Approach 2:
The patent transitions from static preset configuration to dynamic, content-driven configuration. The encoder system continuously adapts its tool selections and restrictions based on real-time content analysis, enabling it to handle varying content classes and characteristics dynamically. This dynamic approach allows the system to optimize encoding parameters for each specific content type without requiring complex manual configuration
Data Source
AI summary
Techniques for content-adaptive encoder configuration are described herein. In accordance with various embodiments, a device (e.g., a content-based toolset configurator) including a processor and a non-transitory memory receives one or more frames in a media stream and a performance target of an encoder. The content-based toolset configurator performs cycles of pre-analysis of the one or more frames to generate content features within the performance target, assigns a content class to the one or more frames based on the content features, a previous classification, and the performance target, and sets configurations of the encoder for encoding the one or more frames corresponding to the content class and the performance target.