Neural Rate-Distortion Modeling for Stable Video Encoding
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Solution Overview
Problem
Existing video coding standards face challenges in accurately modeling rate-distortion relationships, especially for large Group-of-Pictures (GOP) sizes and diverse video content, leading to suboptimal encoding performance and bit-rate fluctuations.
Innovation Solution
A neural network-based rate control scheme that determines model parameters using prediction residuals to refine encoding decisions, incorporating a pre-analysis framework and neural networks to estimate bit-rate and distortion for each coding tree unit, allowing for more accurate bit allocation and stable encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video coding standards are used, then encoding process is simple, but rate-distortion modeling accuracy deteriorates
Solution Approach 1:
A neural network arrangement is introduced as an intermediary component between the video data and the rate-distortion model. The neural network processes prediction residuals and generates model parameters (α and β) that accurately represent the rate-distortion relationship, thereby improving modeling accuracy without requiring fundamental changes to the encoding standard architecture
Solution Approach 2:
The neural network performs preliminary analysis of prediction residuals before the main encoding process. By pre-computing model parameters from residuals in a pre-analysis framework, the system prepares accurate rate-distortion models in advance, enabling more precise bit allocation and encoding decisions throughout the encoding process
2Productivity
If larger GOP sizes are used, then compression efficiency is improved, but rate-distortion performance deteriorates
Solution Approach 1:
The system dynamically adapts rate-distortion model parameters for each coding tree unit based on local video content characteristics. By using neural networks to compute unit-specific parameters α and β rather than global averages, the system maintains accurate rate-distortion modeling even when processing large GOP sizes with diverse video content
Solution Approach 2:
Different rate-distortion model parameters are applied to different coding tree units based on their local characteristics. The neural network analyzes prediction residuals at each unit level and generates localized model parameters, ensuring that each region is encoded with parameters optimized for its specific content rather than using uniform parameters across the entire frame
3Reliability
If conventional encoding methods are used, then processing speed is maintained, but bit-rate stability deteriorates
Solution Approach 1:
The system uses neural network-based rate-distortion models to provide feedback on the relationship between bit-rate and distortion for each coding tree unit. This feedback mechanism enables more accurate bit allocation decisions that maintain stable bit-rate output while improving overall encoding efficiency through optimized parameter selection
Data Source
AI summary
A computer-implemented method for facilitating encoding of video data. The method includes performing an operation to determine prediction residuals associated with a unit of the video data, and, processing, using a neural network arrangement, the prediction residuals associated with the unit of the video data to determine model parameters associated with a rate-distortion model for the unit of the video data. The model parameters are arranged to facilitate encoding of at least the unit of the video data. The method can be applied to multiple ones of such unit of the video data.