Video Encoder Self-Optimization via Distributed Learning
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Solution Overview
Problem
Video codec standards and implementations are suboptimal due to limited data used in design, leading to over-fitted designs that perform poorly in real-world scenarios, as they are typically developed with small test sets and lack adaptation to real-world constraints such as constant bit rate or error resilience.
Innovation Solution
A distributed learning system where video encoders in the field analyze real-world video data to optimize encoding algorithms, using a combination of locally captured video and centrally distributed test sets to adapt and improve encoding processes, allowing for real-time tuning of parameters like mode combinations, bit rate, and quality metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video codec standards are developed with limited test data, then the design process is faster and easier, but the encoding performance in real-world scenarios deteriorates due to over-fitting
Solution Approach 1:
The patent implements preliminary action by having the encoder collect and store video data during normal operation before the optimization phase. This pre-collected data is then used during idle periods to train and optimize encoding parameters, allowing the system to prepare optimization solutions in advance without impacting real-time encoding performance.
Solution Approach 2:
The system employs self-service by enabling the encoder to automatically optimize its own parameters using real-world data it encounters during operation. The encoder analyzes its own performance on stored video portions and adjusts its encoding functions without requiring external intervention or retraining, making the system self-improving over time.
2Stability of the object's composition
If video encoders use fixed encoding parameters, then the implementation is simpler and more stable, but the adaptability to different real-world video content deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning the encoding parameters from static to adaptive. The encoder maintains stable fixed parameters during normal operation but periodically updates these parameters by analyzing real-world video data during idle periods. This creates a dynamic system that adapts to different content types while maintaining operational stability during encoding tasks.
Solution Approach 2:
The system implements feedback by having the encoder evaluate its own performance on stored video portions and use this information to adjust its encoding parameters. The encoder analyzes the results of encoding different video content and feeds this performance data back into the parameter optimization process, continuously improving adaptability based on actual performance outcomes.
3Reliability
If video encoders analyze and optimize parameters during operation, then encoding performance improves, but the processing time and computational load increase
Solution Approach 1:
The patent implements periodic action by scheduling optimization analysis to occur during idle periods rather than continuously during encoding operations. The encoder performs normal encoding tasks during active periods and dedicates idle time to analyzing stored video portions and optimizing parameters, thus separating production and optimization phases to avoid time conflicts.
Solution Approach 2:
The system uses preliminary action by pre-collecting and storing video data during normal encoding operations. This pre-prepared data is then available for immediate analysis during idle periods without requiring additional real-time processing, allowing the encoder to optimize parameters without adding to the critical processing path timing.
Data Source
AI summary
In one embodiment, one or more portions of video content that were encoded by a video encoder are identified. The one or more portions of video content are to be analyzed for improving encoding performance of the video encoder. The one or more portions of the video content are stored. During an idle time period of the video encoder, the one or more portions of video content are analyzed in combination with a test set stored by the video encoder in order to modify one or more functions of an encoding process used by the video encoder. Based on the analysis, one or more modifications to the encoding process are determined that result in improved performance of the video encoder.