Motion Compensation Using Pre-Trained Neural Networks
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Solution Overview
Problem
Current video encoding technologies face challenges in achieving optimal motion compensation due to the computational expense of finding the most accurate reference blocks, which limits compression performance and results in inefficient data transmission.
Innovation Solution
The use of pre-trained neural networks to transform reference blocks into predicted blocks, allowing for more accurate motion compensation and reducing the residual data required for encoding, with the option to select content-specific neural networks based on block properties and metadata for improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion estimation algorithms are used to find optimal reference blocks, then motion compensation accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks offline to learn optimal motion compensation mappings. The trained network parameters are then stored and reused during actual video encoding/decoding, separating the computationally intensive learning phase from the real-time application phase. This allows high accuracy motion compensation without real-time computational complexity.
Solution Approach 2:
The patent substitutes traditional mechanical motion estimation algorithms (block matching, optical flow) with a neural network-based system. The neural network learns complex motion patterns from training data and directly predicts motion vectors and compensated frames, replacing iterative algorithmic approaches with a learned model that achieves higher accuracy with lower real-time computational cost.
2Manufacturing precision
If more reference blocks are analyzed to improve prediction accuracy, then compression performance is improved, but data transmission requirements and processing overhead increase
Solution Approach 1:
The patent changes the parameter representation by using neural networks to directly predict motion vectors and compensated blocks instead of transmitting multiple reference block candidates. The network processes reference blocks internally and outputs optimized predictions, reducing the quantity of data that needs to be transmitted while maintaining or improving prediction accuracy through learned transformations.
3Productivity
If content-specific neural networks are selected based on block properties, then motion compensation efficiency is improved, but system complexity and metadata requirements increase
Solution Approach 1:
The patent applies local quality by selecting different pre-trained neural networks based on local block properties such as motion characteristics, content type, and reference frame differences. Each block or region can use a specialized network optimized for its specific characteristics, improving overall efficiency while managing complexity through conditional selection rather than universal processing.
Solution Approach 2:
The patent introduces dynamics by making the neural network selection adaptive and content-dependent. The system dynamically chooses appropriate networks based on block properties, reference frame timing, and motion characteristics, allowing the motion compensation process to adapt to varying video content requirements rather than using a static single-network approach.
Data Source
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AI summary
The present invention relates to the use of machine learning to improve motion compensation in video encoding. According to a first aspect, there is provided a method for motion compensation in video data using hierarchical algorithms, the method comprising the steps of: receiving one or more original blocks of video data and one or more reference blocks of video data; determining, using one or more hierarchical algorithms, one or more predicted blocks of video data from the one or more reference blocks of video data; and calculating one or more residual blocks of video data from the one or more predicted blocks of video data and the one or more original blocks of video data.