Unified Image Encoding Model Reducing Computational Complexity
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Solution Overview
Problem
Existing video encoding systems face challenges in efficiently processing multiple image processing operations that utilize respective machine learning algorithms, leading to cumulative computational requirements that exceed the system's capabilities.
Innovation Solution
The proposed method involves obtaining depth data for input image data and performing multiple data processing operations based on this depth data, which is used as a common denominator across various image encoding workflow operations, thereby reducing computational complexity and sharing computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple machine learning algorithms are used for respective image processing operations, then image processing quality is improved, but cumulative computational requirements exceed system capabilities
Solution Approach 1:
The patent combines multiple separate machine learning algorithms into a unified machine learning model that performs multiple image processing operations simultaneously. This consolidation reduces the cumulative computational overhead of running separate algorithms while maintaining the quality benefits of multiple processing functions.
Solution Approach 2:
The unified machine learning model is designed to perform multiple image processing operations (such as denoising, super-resolution, and color enhancement) within a single framework. This multi-functional approach allows the system to achieve high image processing quality across different operations without requiring separate specialized algorithms for each task.
2Adaptability or versatility
If multiple separate machine learning algorithms are used for different image processing operations, then processing coverage is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple separate machine learning algorithms into a single unified model, reducing system complexity by eliminating the need to manage, deploy, and coordinate multiple independent algorithms. The unified model simplifies the system architecture while maintaining broad processing coverage through its multi-functional design.
Solution Approach 2:
The unified machine learning model provides universal processing coverage by incorporating multiple image processing capabilities within a single framework. This approach maintains versatility across different processing tasks while reducing device complexity compared to implementing separate specialized algorithms for each operation.
3Productivity
If multiple machine learning algorithms are deployed, then processing capability is improved, but computational resource management becomes difficult
Solution Approach 1:
The patent consolidates multiple machine learning algorithms into a unified model that can be managed as a single computational resource. This merging simplifies resource allocation, memory management, and computational scheduling while maintaining the enhanced processing capability that results from combining multiple processing functions.
Solution Approach 2:
The unified machine learning model provides multi-functional processing capability within a single resource framework, making computational resource management more straightforward. The system can allocate resources to the unified model once rather than managing separate allocations for multiple algorithms, reducing management overhead while maintaining high processing capability.
Data Source
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AI summary
A method of processing input image data of a set of images to be encoded is proposed, which comprises: obtaining depth data of the input image data, performing a first data processing on the input image data based on the depth data of the input image data, wherein the first data processing is performed as part of an image encoding workflow of data processing for encoding the input image data, performing a second data processing on the input image data based on the depth data of the input image data, wherein the second data processing is performed as part of the image encoding workflow, and encoding the input image data according to the image encoding workflow based on data output by the first data processing and the second data processing.