Intra-Prediction Simplification Using Reference-Independent Components
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Solution Overview
Problem
The complexity of Neural Networks (NN) in video codecs makes it difficult to interpret and simplify the intra-prediction process, which is essential for efficient video compression, especially in the context of the Versatile Video Coding (VVC) standard.
Innovation Solution
The proposed solution involves expressing sample-wise prediction as a sum of two components: one dependent on reference samples and another independent of them, using weights and fixed parameters, which can be derived from a Look-Up-Table or learned during decoding, allowing for a simplified and clear approach to intra-prediction that achieves similar results to NN-based methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Neural Networks are used for intra-prediction in video codecs, then prediction accuracy is improved, but interpretability and simplicity of the process deteriorate
Solution Approach 1:
The patent creates simplified copies of the Neural Network's predictive behavior using conventional video coding tools. Instead of directly implementing complex NN architectures, the invention copies the essential predictive patterns learned by NNs and implements them using standard intra-prediction modes, lookup tables, and parameter sets that are interpretable and compatible with existing video codec frameworks.
Solution Approach 2:
The patent replaces expensive, complex Neural Network models with simpler, computationally lighter alternatives. By using conventional prediction modes with learned parameters stored in lookup tables, the system achieves comparable prediction accuracy without the computational burden and interpretability issues of full NN implementations, effectively using disposable simplified models instead of persistent complex ones.
2Productivity
If Neural Networks are used for intra-prediction, then compression efficiency is improved, but computational complexity and processing overhead increase
Solution Approach 1:
The patent substitutes the mechanical Neural Network computation system with a hybrid approach that replaces heavy NN inference operations with conventional video coding operations. By using intra-prediction modes, lookup tables, and parameter-based methods instead of full NN forward propagation, the system maintains compression efficiency while reducing computational complexity to levels suitable for real-time encoding and decoding.
Solution Approach 2:
The patent changes the parameters from complex NN weights and activations to simpler parameters such as prediction mode indices, lookup table indices, and scalar coefficients. These parameter changes enable the system to achieve NN-like prediction performance using conventional arithmetic operations that are computationally efficient and easily implemented in hardware or software video codecs.
3Measurement precision
If complex NN-based intra-prediction is implemented, then prediction performance is improved, but ease of implementation and integration with existing codecs deteriorates
Solution Approach 1:
The patent creates a universal intra-prediction framework that can perform both conventional prediction and NN-inspired prediction using the same codebase and processing pipeline. By designing the system to support multiple prediction modes (conventional and learned) within a unified architecture, the invention achieves NN-level performance while maintaining ease of implementation and seamless integration with existing video codec standards and implementations.
Data Source
AI summary
Intra-prediction modes are defined in that, predictions obtained in accordance with those modes are based on a sum of a first component dependent on a set of reference samples and a second component not dependent on the reference samples.