Graph-Based Video Transform Optimization for High-Resolution Coding
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Solution Overview
Problem
Existing video processing technologies struggle with high spatial resolution, high frame rate, and high dimensionality of scene representation, leading to increased memory storage, memory access rate, and processing power demands, and require more efficient coding tools for next-generation video content.
Innovation Solution
A method for designing robust transforms using graph-based representations, generating optimized transforms through data clustering and multiple graph-based models, minimizing the squared sum of off-diagonal elements, and adapting to different statistical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single model transform is used, then the transform design is simple, but it cannot respond to the overall change in data and is not appropriate for general use
Solution Approach 1:
The patent applies dynamics by making the transform adaptive to different data models through multiple graph-based models. The system dynamically selects or switches between different transform models based on the characteristics of the input data, allowing the transform to respond to overall changes in data rather than being fixed to a single model assumption.
Solution Approach 2:
The patent implements universality by designing multiple graph-based transform models that can handle different types of data distributions. This multi-functional approach allows a single transform system to be appropriate for general use across various data models, not just optimized for one specific model.
2Manufacturing precision
If high spatial resolution and high frame rate are implemented, then image quality improves, but memory storage, memory access rate, and processing power demands increase tremendously
Solution Approach 1:
The patent applies parameter changes by utilizing multiple graph-based models with different parameters to represent video data at different resolutions and frame rates. By adjusting the model parameters based on the specific requirements, the system can maintain image quality while optimizing resource consumption for different spatial resolutions and frame rates.
Data Source
AI summary
The present invention, with respect to a method of processing video data, provides a method of processing video data, provides a method characterized by comprising the steps of: performing a clustering for the video data; generating at least one data cluster as a result of the clustering; generating at least one Graph laplacian matrix corresponding to the at least one data cluster; performing conversion optimization on the basis of multiple graph-based models, wherein the multiple graph-based models respectively include at least one graph laplacian matrix; and generating an optimized conversion matrix according to the results of performing the conversion optimization.