ML-Based QP Estimation for Intra and Scene Change Encoding
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Solution Overview
Problem
Existing video encoding technologies face challenges in accurately predicting quantization parameters (QP) for intra and scene change frames, leading to poor video quality and buffer overflow/underflow due to the lack of correlation with previous frames, which complicates bit rate control (BRC) and requires computationally expensive second pass encoding.
Innovation Solution
A machine learning-based approach for QP selection using feature vectors generated from look-ahead analysis, including average block variance, proportion of smooth blocks, and prediction distortion, combined with a pretrained neural network for accurate first and second pass encoding adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional BRC techniques are used to control frame size by adjusting QP value, then compression efficiency can be maintained, but accurate QP prediction for intra frames and scene change frames becomes difficult leading to poor video quality and buffer overflow/underflow
Solution Approach 1:
The system performs look-ahead analysis on future frames before encoding the current frame to predict QP values. This preliminary action allows the encoder to anticipate the bit cost of intra frames and scene change frames, enabling accurate QP selection that prevents buffer overflow/underflow while maintaining target frame size control.
Solution Approach 2:
The patent introduces an intermediate QP adjustment mechanism that operates between traditional BRC and the actual encoding process. This intermediary layer uses look-ahead statistics to compute refined QP values for intra frames and scene change frames, bridging the gap between coarse traditional BRC and the need for precise frame-size control.
2Measurement precision
If multiple pass encoding is used to obtain accurate QP for target frame size, then compression efficiency improves, but computational complexity and encoding time increase significantly
Solution Approach 1:
The system performs necessary statistical analysis and QP prediction in a preliminary look-ahead phase before the actual encoding pass. By pre-computing QP values for intra frames and scene change frames based on future frame statistics, the system eliminates the need for multiple re-encoding passes, achieving accurate frame size control in a single pass.
Solution Approach 2:
The patent enables continuous single-pass encoding by integrating look-ahead analysis and QP adjustment into the encoding workflow. The system continuously collects statistics from look-ahead frames and immediately applies QP adjustments during the encoding process, maintaining uninterrupted encoding flow without stopping for multiple passes.
3Speed
If hardware solutions are used for video encoding, then real-time processing capability is achieved, but additional gate counts are required to collect BRC statistics making the solution more complex
Solution Approach 1:
The patent designs the hardware BRC statistics collector to perform multiple functions using the same circuitry. The same hardware blocks that perform video encoding also collect BRC statistics, and the look-ahead analysis reuses existing decode and encode pipelines. This multi-functionality reduces the need for dedicated separate hardware for statistics collection.
Solution Approach 2:
The encoding hardware automatically collects its own BRC statistics during the normal encoding process without requiring external monitoring circuits. The system self-gathers information about frame bit costs, QP values, and buffer usage as part of its regular operation, eliminating the need for additional gate counts for separate statistics collection.
Data Source
AI summary
Techniques related to quantization parameter estimation for coding intra and scene change frames are discussed. Such techniques include generating features based on an intra or scene change frame including a proportion of smooth blocks and one or both of a measure of block variance and a prediction distortion, and applying a machine learning model to generate an estimated quantization parameter for encoding the intra or scene change frame.