Deep Prediction Refinement Using Neural Networks for Video Compression
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Solution Overview
Problem
Existing video encoding and decoding technologies face challenges in achieving high compression efficiency due to limitations in prediction refinement methods, particularly in handling spatial and temporal redundancy, which can lead to suboptimal prediction accuracy and increased bit rates.
Innovation Solution
The use of Neural Networks for determining predictions based on motion-compensated regions, incorporating deep learning techniques to refine predictions by leveraging first and second regions, enabling more accurate encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction methods are used, then device complexity is low, but prediction accuracy deteriorates leading to suboptimal compression efficiency
Solution Approach 1:
The patent replaces traditional mechanical prediction algorithms with a Neural Network system that uses first and second motion-compensated regions to generate predictions. This substitution of mechanical computation with neural network processing resolves the contradiction by achieving higher prediction accuracy through learned patterns while managing complexity through efficient network architecture design.
Solution Approach 2:
The patent introduces motion-compensated regions as intermediary data structures that feed into the Neural Network. These regions serve as mediators between the input video data and the prediction output, enabling the network to process spatial and temporal information effectively. This intermediary approach improves prediction accuracy while keeping the overall system complexity manageable through structured data flow.
2Productivity
If prediction refinement is improved, then compression efficiency is enhanced, but bit rate increases due to additional data
Solution Approach 1:
The patent extracts only the essential motion-compensated regions (first and second regions) needed for accurate prediction, discarding redundant information. This extraction approach enables high compression efficiency by focusing computational resources on the most relevant data while keeping the bit rate low through selective information transmission.
Solution Approach 2:
The patent applies partial action by using a streamlined Neural Network architecture that processes only the necessary features from motion-compensated regions. Rather than processing all possible video data, the system focuses on key regions and features, achieving good compression efficiency without the overhead of excessive processing that would increase bit rate.
3Measurement precision
If neural networks are used for prediction, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the prediction task into distinct components: first motion-compensated region processing, second motion-compensated region processing, and Neural Network integration. This segmentation allows each component to be optimized independently, improving overall prediction accuracy while managing computational complexity through modular processing stages.
Solution Approach 2:
The patent performs preliminary motion compensation to generate the first and second regions before feeding them to the Neural Network. This preliminary action prepares the data in an optimized format that reduces the computational burden on the neural network, thereby improving prediction accuracy while controlling overall computational complexity through pre-processing.
Data Source
AI summary
A method and an apparatus for deep prediction refinement are disclosed. A first motion-compensated region for a block of a picture and a second region for said block are obtained. A prediction for said block is determined using a Neural Network that uses said first motion-compensated region and said second region.