Composite Image Refinement Using Multi-Model Harmonization
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Solution Overview
Problem
Conventional video and image editing techniques are labor-intensive and prone to errors, making them inefficient for enhancing visual quality or inserting virtual advertisements.
Innovation Solution
A set of machine-learning models, including a first model for coarse harmonization, a second model for color mapping, and a third model for generating a sparse feature, are applied to input images to refine them by adjusting foreground and background data, utilizing techniques like depth estimation, color mapping, and sparse feature generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional video and image editing techniques are used, then editing can be performed, but the process is labor-intensive and prone to errors
Solution Approach 1:
The patent replaces manual mechanical editing operations with machine-learning models that automatically perform coarse harmonization, color mapping, and sparse feature generation. This substitution eliminates human labor intensity while maintaining high reliability through automated consistent processing of image data.
Solution Approach 2:
The system enables self-service editing where the machine-learning models autonomously analyze image data, identify foreground and background regions, and apply appropriate transformations without human intervention. The models self-correct errors by learning from training data, reducing both labor intensity and error rates.
2Manufacturing precision
If machine-learning models are applied for image refinement, then visual quality is enhanced and errors are reduced, but processing complexity increases
Solution Approach 1:
The patent segments the image refinement task into three distinct machine-learning models: coarse harmonization model, color mapping model, and sparse feature generation model. Each model specializes in a specific aspect of image processing, achieving high visual quality through divided expertise while managing complexity by isolating functions into separate modular components.
Solution Approach 2:
The patent introduces intermediate representations (coarse harmonized image, color mapped image, sparse features) that serve as mediators between the input image and final refined output. These intermediaries break down the complex transformation into manageable stages, each handled by a specialized model, thereby achieving high precision without overwhelming system complexity.
3Measurement precision
If multiple machine-learning models are used for different processing stages, then refinement accuracy is improved, but computational resources required increase
Solution Approach 1:
The patent divides the refinement accuracy requirement into three sequential processing stages, each handled by a dedicated model. This segmentation allows each model to be optimized for its specific task with appropriate computational resources, rather than requiring one massive model to handle all aspects, thereby improving accuracy efficiently.
Solution Approach 2:
The patent applies preliminary actions in sequence: coarse harmonization first establishes basic consistency, then color mapping refines color accuracy, and finally sparse feature generation adds detailed precision. Each preliminary action prepares the data for the next stage, ensuring that computational resources are used efficiently at each step rather than requiring excessive resources in a single pass.
Data Source
AI summary
A computer-implemented method can be used to generate a composite image. Image data can be received, and a low-resolution composite image can be generated. A first machine-learning model can be applied to the low-resolution image and to a mask of the image data to generate a second image. A second machine-learning model can be applied to the image data to perform color mapping from an optimal patch to a masked foreground area of the image data. The color mapping can be used to generate a third image. A composite image can be generated using the second image and the third image.