Dynamic Codec Bitrate Control for Consistent Video Quality
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Solution Overview
Problem
Conventional video encoding techniques for embedded devices like action cameras struggle to maintain consistent image quality across varying content complexities due to fixed bitrate settings, leading to inefficient use of resources and suboptimal encoding quality.
Innovation Solution
Dynamically configure the codec to adjust bitrate based on pixel and encoding features, using machine learning models to predict image quality and maintain consistent encoding quality through techniques such as iso-quality encoding and dual resolution encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed bitrate settings are used in video encoding, then device complexity is reduced, but image quality consistency deteriorates across varying content complexities
Solution Approach 1:
The patent implements dynamic bitrate allocation that adjusts encoding parameters in real-time based on content complexity analysis. The system transitions from static fixed bitrate settings to dynamic adaptive encoding, where bitrate is modulated according to measured content characteristics such as motion magnitude, texture complexity, and scene changes, thereby maintaining image quality consistency across varying content types.
Solution Approach 2:
The system changes encoding parameters (bitrate, quantization values, prediction modes) based on analyzed content features. By monitoring content complexity metrics and adjusting encoding parameters accordingly, the system optimizes the balance between compression efficiency and image quality, allocating higher bitrates to complex scenes and lower bitrates to simple scenes.
2Manufacturing precision
If dynamic bitrate adjustment based on content complexity is implemented, then image quality consistency is improved, but device complexity increases
Solution Approach 1:
The patent segments the video content into distinct complexity regions or scenes based on analyzed features such as motion, texture, and temporal changes. By dividing the video stream into segments with similar characteristics, the system can apply optimized encoding parameters to each segment, managing complexity through localized processing rather than global analysis.
Solution Approach 2:
The system performs preliminary analysis of content complexity features (motion vectors, texture metrics, scene detection) before the actual encoding process. This pre-analysis allows the encoder to pre-determine optimal bitrate allocation and encoding parameters for upcoming frames, reducing real-time computational burden during the encoding phase while maintaining quality consistency.
3Manufacturing precision
If image analysis and metrics are used for encoding decisions, then encoding quality is improved, but processing time increases due to computational complexity
Solution Approach 1:
The patent applies partial image analysis by selectively analyzing only the most critical content features (such as motion magnitude, key scene changes, or regions of high visual importance) rather than performing exhaustive analysis of all image data. This selective approach captures sufficient information for encoding decisions while significantly reducing computational complexity and processing time.
Solution Approach 2:
The system replaces complex mechanical image processing with simplified computational models or heuristic rules for estimating content complexity. By using efficient algorithms and approximations instead of computationally intensive analysis, the system achieves acceptable encoding quality decisions within real-time processing constraints.
4Productivity
If look-forward/look-backward comparisons between frames are performed, then encoding efficiency is improved, but real-time processing capability deteriorates
Solution Approach 1:
The patent performs preliminary analysis of content complexity using only past and current frame information, avoiding the need for future frame (look-forward) analysis. By making encoding decisions based on historical and present data rather than requiring future frame comparison, the system maintains real-time processing capability while achieving efficient compression through adaptive bitrate allocation based on observed content patterns.
Data Source
AI summary
Systems, apparatus, and methods for dynamic encoder configuration. In one exemplary embodiment, a machine-learning model uses pixel features and encoding features from previous stages of an image processing pipeline (IPP) to dynamically adjust bitrate. The machine-learning model is trained to select bitrate adjustments for an encoder such that the expected image quality of a video stream remains at a selected quality level (e.g., SSIM, VMAF, VIF, HVS-PSNR, etc.). Conventional dynamic encoding solutions are focused on encode-once-deliver-often (best-effort) applications, the exemplary IPP is designed for real-time applications that may not have the benefit of actual subsequent encoding quality analysis; instead proxy data (pixel features and encoding features) that are representative approximations of image complexity are used.