Initial Quantization Parameter Prediction in Video Coding
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Solution Overview
Problem
Existing methods for determining the initial quantization parameter (QP) in video coding face challenges due to limited bit resources and quality dependency between frames, leading to inaccurate and unreliable bit allocation, which affects rate-distortion performance and stability in video coding.
Innovation Solution
A machine learning-based framework using support vector regression (SVR) for predicting initial QP, where learning labels are built to maximize rate-distortion performance by utilizing target bits per pixel for remaining frames, and a clipping and decision approach is applied for fast QP adaptation and quality smoothness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If empirical model parameters are used for initial QP determination, then the method is simple to implement, but the coding performance reliability is poor across different videos
Solution Approach 1:
The system performs self-learning by automatically optimizing model parameters through machine learning algorithms. The model learns from training data and automatically adjusts its parameters to achieve optimal coding performance across different video sequences, eliminating the need for manual empirical parameter setting while maintaining reliability.
Solution Approach 2:
The patent transforms fixed empirical parameters into dynamic learnable parameters. By using machine learning models, the system can adapt parameters based on video content characteristics, resolution, and other factors, thereby achieving reliable coding performance across diverse video sequences without manual intervention.
2Ease of operation
If conventional distortion evaluation is used to select initial QP, then the selection process is simple, but the rate-distortion performance is suboptimal due to bit rate mismatch
Solution Approach 1:
The system implements feedback mechanisms where the machine learning model learns from training data that includes actual rate-distortion outcomes. The model uses this feedback to adjust its predictions and select initial QP values that optimize rate-distortion performance rather than relying on simple distortion evaluation.
Solution Approach 2:
The patent performs preliminary learning and optimization during a training phase before actual video coding. The machine learning model is pre-trained with optimal initial QP values and their corresponding features, so during runtime, the system can quickly select appropriate initial QP values without performing complex real-time optimization, thus achieving both good performance and efficiency.
3Ease of operation
If fixed QP values are used for initial intra frames, then the rate control initialization is simple, but the bit allocation cannot adapt to different video complexities and resolutions
Solution Approach 1:
The patent transforms static fixed QP values into dynamic adaptive initial QP values. The machine learning model takes video-specific features such as resolution, complexity metrics, and content characteristics as inputs and generates customized initial QP values for each video sequence, enabling the system to adapt to different video complexities and resolutions while maintaining simple operation through automated decision-making.
Data Source
AI summary
A machine learning based initial quantization parameter (QP) prediction method, which can effectively optimize RC performance A machine learning framework for initial QP prediction is proposed, where learning labels are built with the criterion of maximizing rate-distortion (RC) performance, which is proved to be much more effective than the QP determination method with the only consideration on sum of the absolute transformed difference (SATD) complexity. Instead of target bits per pixel for intra frame, target bits per pixel for remaining frames is used as sample data to avoid empirically setting intra frame bit allocation, thus improve the prediction accuracy as the real-time updated remaining bits can better reflect the real-time requirements on the level of QPs. In addition, a clipping and decision approach based on the previous initial QP and the target bits per pixel for all remaining frames is proposed, which can help fast QP adaption and quality smoothness.