MAD Prediction for Video Encoding Rate Control
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Solution Overview
Problem
In conventional video coding systems, predicting the Mean Absolute Difference (MAD) for rate control in H.264 is inaccurate for bitstreams with temporal and quality scalability, leading to inappropriate selection of the Quantization Parameter (QP) and poor rate control performance.
Innovation Solution
A method using two regressors in a single linear regression model to predict MAD, where the first regressor is the MAD of the closest frame across higher, lower, or current temporal layers, and the second regressor is the MAD of the closest frame with similar rate distortion characteristics, to determine an appropriate QP for encoding using a quadratic rate-quantizer model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a linear regression model using actual MAD of previous stored frames is used for MAD prediction, then the prediction process is simple, but the prediction accuracy deteriorates for bitstreams with temporal and quality scalability
Solution Approach 1:
The patent dynamically adapts the linear regression model by introducing multiple regressors (MAD from previous frames at different temporal layers and quality levels) and selectively using them based on the specific encoding context. This allows the model to adapt to different scalability scenarios (temporal, quality, or both) rather than using a fixed simple model, thereby improving prediction accuracy while maintaining reasonable complexity through conditional selection rather than exhaustive processing.
Solution Approach 2:
The patent changes the parameters of the regression model by introducing multiple regressors with different characteristics (temporal layer indices, quality level indices) and dynamically selecting which parameters to use based on the current frame's position in the scalability hierarchy. This parameter adaptation allows the same basic linear regression framework to handle diverse scalability scenarios accurately.
2Ease of operation
If the wrong regressor is selected in a linear regression model for scalable video coding, then the selection process is straightforward, but the MAD prediction accuracy deteriorates leading to inappropriate QP selection
Solution Approach 1:
The patent implements dynamic regressor selection based on the current frame's temporal layer and quality level indices. The selection process adapts to the specific scalability scenario by comparing the current frame's indices with stored regressor indices, automatically choosing the most appropriate regressor (from previous temporal layers, quality levels, or both) rather than using a fixed selection rule. This dynamic adaptation ensures accurate MAD prediction across different scalability configurations.
Solution Approach 2:
The patent uses the temporal layer index and quality level index as dynamic parameters to select the appropriate regressor. By changing the selection criteria based on these parameters, the system can adapt to different scalability scenarios (temporal-only, quality-only, or both) and select the regressor that best matches the current encoding context, thereby maintaining high prediction accuracy.
3Device complexity
If inaccurate MAD prediction is used for rate control, then the rate control process is simple, but the Quantization_parameter selection becomes inappropriate resulting in poor rate control performance
Solution Approach 1:
The patent implements a feedback mechanism where the MAD prediction accuracy directly influences the QP selection process. By using multiple regressors and dynamically selecting the most appropriate one based on temporal and quality indices, the system ensures accurate MAD prediction which then feeds into the rate control algorithm for appropriate QP selection. This feedback loop between accurate MAD prediction and QP selection improves overall rate control performance while maintaining a relatively simple rate control structure.
Solution Approach 2:
The patent changes the input parameters to the rate control model by providing accurate MAD predictions derived from dynamically selected regressors. This accurate MAD value, which reflects the actual complexity of the current frame considering temporal and quality scalability, enables the rate control algorithm to select more appropriate QP values, thereby improving rate control reliability without significantly increasing complexity.
Data Source
AI summary
Mean absolute difference (MAD) prediction for video encoding may be provided. Upon receiving a video stream comprising a plurality of quality layers, a first quantization parameter (QP) may be selected for a first frame of the video stream according to a second QP associated with a second frame and a third QP associated with a third frame. The first frame may then be encoded according to the selected first QP.