Video Encoding Rate Control Using Reconstructed Quality Feedback
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Solution Overview
Problem
Current video encoding rate control algorithms fail to accurately adapt to the type of content being encoded due to a lack of reliable end-user perception feedback and fixed, pre-calculated tuning, leading to suboptimal quality and bitrate trade-offs.
Innovation Solution
Implementing a trained, probabilistic quality model that determines video encoding quality based on reconstructed pixel data, allowing the rate control algorithm to dynamically adjust encoding rates and improve accuracy independently of input pixel data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed, pre-calculated tuning is used in rate control algorithms, then device complexity is reduced, but adaptability to different content types deteriorates
Solution Approach 1:
The rate control algorithm transitions from static, pre-calculated tuning to dynamic adaptation by incorporating real-time quality measurements. The system continuously adjusts encoding parameters based on measured quality feedback from reconstructed video, enabling the algorithm to adapt to different content types during operation rather than relying on fixed offline tuning.
Solution Approach 2:
A feedback loop is established where quality measurement results from reconstructed video are fed back to the rate control algorithm. This closed-loop system uses the quality measurements to dynamically adjust encoding parameters, allowing the system to adapt to content characteristics without requiring complex pre-calculated tuning for each content type.
2Measurement precision
If advanced quality measurement calculations (SSIM, VMAF) are used, then measurement precision is improved, but the ability to capture end-user perception accurately deteriorates
Solution Approach 1:
The system uses the reconstructed video output itself as the basis for quality measurement, creating a self-contained evaluation loop. By measuring quality directly from the reconstructed video that will be displayed to users, the system captures actual user-perceived quality rather than relying on theoretical metrics that may not correlate with human perception.
3Device complexity
If rate control algorithm lacks quality feedback, then device complexity is reduced, but productivity in terms of quality-bitrate optimization deteriorates
Solution Approach 1:
The system implements a feedback mechanism where quality measurements from reconstructed video are continuously fed back to the rate control algorithm. This enables automatic optimization of the quality-bitrate tradeoff by adjusting encoding parameters based on actual measured quality, improving productivity without requiring complex manual tuning or pre-calculated settings.
Data Source
AI summary
The disclosed computer-implemented method for video encoding rate control can include governing, by at least one processor, a video encoding rate at least partly in response to video encoding quality information. The method can additionally include generating, by the at least one processor, an encoded video data bitstream based on input pixel data and according to the video encoding rate. The method can also include determining, by the at least one processor, the video encoding quality information based on reconstructed pixel data. Various other methods, systems, and computer-readable media are also disclosed.