Dynamic Filter Selection for Encoding and Decoding
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Solution Overview
Problem
Existing encoding and decoding technologies are limited by hard-coded algorithms in hardware, restricting flexibility and versatility in processing content, especially for user endpoints which cannot easily change or update encoding/decoding algorithms.
Innovation Solution
A cloud service system manages a filter repository with machine-learned encoder and decoder filters that can be dynamically selected and deployed on user devices based on specific hardware configurations and functionality attributes, allowing for flexible processing of various content types without altering the hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-defined algorithms are hard-coded in hardware, then reliability is improved, but adaptability deteriorates
Solution Approach 1:
The patent implements dynamic algorithm selection by allowing the system to switch between different encoding and decoding algorithms based on content characteristics. The encoder can select from multiple pre-trained neural network models (e.g., different compression ratios, quality levels) and the decoder can adaptively choose the appropriate decoding strategy, making the system flexible while maintaining hardware-based reliability.
Solution Approach 2:
The system changes operational parameters by using multiple pre-trained models with different configurations stored in the hardware. Instead of changing the hardware architecture, the system selects different parameter sets (model weights, compression factors, quality settings) to adapt to various encoding/decoding requirements while maintaining the same reliable hardware platform.
2Device complexity
If hardware configuration is fixed, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent makes the hardware universal by incorporating a library of multiple pre-trained neural network models that can handle different content types and processing requirements. The same hardware platform can perform various functions (different compression ratios, quality levels, content types) by selecting appropriate models from the library, eliminating the need for multiple specialized hardware configurations.
Solution Approach 2:
The system performs preliminary action by pre-training multiple neural network models with different configurations before deployment. These pre-trained models are stored in the hardware, allowing the system to quickly adapt to different encoding/decoding requirements without requiring real-time training or complex runtime adjustments, thus maintaining simple hardware while achieving high versatility.
3Adaptability or versatility
If multiple algorithms are supported, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent segments the algorithm library into multiple independent pre-trained models, each optimized for specific encoding or decoding tasks. The filter repository organizes these models as separate, manageable units with clear interfaces, allowing the system to support multiple algorithms while keeping each individual model simple and well-defined. This segmentation makes it easier to manage, update, and select appropriate models without overwhelming complexity.
Data Source
AI summary
A cloud service system manages a filter repository including filters for encoding and decoding media content (e.g. text, image, audio, video, etc.). The cloud service system may receive a request from a client device to provide a filter for installation on a node such as an endpoint device (e.g. pipeline node). The request includes information such as a type of bitstream to be processed by the requested filter. The request may further include other information such as hardware configuration and functionality attribute. The cloud service system may access the filter repository that stores the plurality of filters including encoder filters and decoder filters and may select a filter that is configured to process the type of bitstream identified in the request and provide the selected filter to the client device.