Neural Network Quantization Parameter Estimation for Video Encoding
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Solution Overview
Problem
Current bit rate control techniques for video encoding, particularly in scene change scenarios, face challenges in accurately predicting the quantization parameter (QP) due to lack of correlation with previous frames, leading to poor video quality and buffer overflows/underflows, with existing methods either being computationally expensive or inaccurate.
Innovation Solution
A pre-trained neural network is employed to predict the QP by extracting features from video frames, including grid-based prediction distortion and variance, combined with target bitrate and resolution, to generate an accurate QP for enhanced compression efficiency and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple pass approaches are used to predict QP, then QP prediction accuracy is improved, but computation complexity and encoding time increase significantly
Solution Approach 1:
The patent pre-calculates and stores QP values for various picture types (I, P, B frames) and scene change conditions in lookup tables during an offline training phase. During actual encoding, the system simply queries these pre-computed tables rather than performing complex multi-pass optimization, thus achieving high accuracy without runtime computational burden
Solution Approach 2:
The patent creates simplified copies of the complex multi-pass QP optimization process by training neural networks offline to learn the optimal QP mappings. The trained model weights and lookup tables serve as copies of the expensive multi-pass algorithm, enabling fast inference that replicates the accuracy of multiple passes without the iterative computation
2Device complexity
If heuristic and empirical approaches are used for QP prediction, then computation complexity is reduced, but QP prediction accuracy deteriorates
Solution Approach 1:
The patent replaces traditional heuristic and empirical QP prediction methods (which rely on hand-crafted rules and simple statistics) with a data-driven neural network model. The neural network learns optimal QP prediction patterns from training data, substituting the mechanical rule-based system with a learned model that achieves superior accuracy while maintaining low computational complexity during encoding
Solution Approach 2:
The patent transforms the QP prediction problem from using simple picture-type identifiers to using rich feature vectors including scene change detection results, picture type, and encoded bitrates. These enhanced parameters feed into the neural network, enabling more accurate predictions without significantly increasing computational complexity during encoding
3Productivity
If inaccurate QP prediction is used for scene change frames, then encoding speed is maintained, but video quality deteriorates and buffer overflows/underflows occur
Solution Approach 1:
The patent introduces scene change detection as an intermediary step that identifies frames requiring accurate QP prediction. When a scene change is detected, the system queries pre-computed lookup tables or uses the neural network to obtain accurate QP values. This intermediary mechanism ensures high video quality and buffer stability for critical scene change frames while maintaining fast encoding for normal frames
Solution Approach 2:
The system pre-computes optimal QP values for various scene change scenarios and stores them in lookup tables before encoding begins. During encoding, when a scene change frame is detected, the system immediately retrieves the pre-computed QP value without performing complex calculations, thus maintaining both high encoding speed and reliable video quality for scene changes
Data Source
AI summary
Techniques related to quantization parameter estimation for video coding are discussed. Such techniques may include generating features using a picture of input video received for encoding and applying a neural network to a feature vector including the features, a target bitrate, and a resolution of the picture to generate an estimated quantization parameter for encoding the picture.