Differentiable Image Editing Controls for Non-Destructive Tuning
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Solution Overview
Problem
Conventional image processing techniques are non-differentiable, requiring users to iteratively guess and check slider values, which is time-consuming and computationally expensive, and existing machine learning approaches fail to provide intuitive control over editing, leading to destructive or inaccurate image processing.
Innovation Solution
A differentiable emulation framework is developed using an emulator-translator system to predict control parameters for non-destructive image editing, allowing for efficient and explainable adjustments by training an emulator to match non-differentiable image processing software and a translator to predict corresponding parameters for various outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional non-differentiable image processing software is used with iterative slider adjustment, then user control over editing is achieved, but computational expense and time consumption increase significantly
Solution Approach 1:
The patent creates a differentiable copy (emulator) of the non-differentiable image processing software. This emulator replicates the visual output and control behavior of the original software but uses differentiable operations, enabling gradient-based optimization instead of iterative slider adjustment. The emulator is trained to match the original software's output, allowing users to benefit from both automated optimization and intuitive control.
Solution Approach 2:
The patent introduces a translator as an intermediary component that bridges the differentiable emulator and the non-differentiable original software. The translator converts control parameters between the two systems, allowing the differentiable emulator to generate optimized parameters that can be applied to the original software, thus enabling automated tuning while maintaining compatibility with the user interface.
2Ease of operation
If conventional non-differentiable image processing software is used with iterative slider adjustment, then user control over editing is achieved, but computational expense increases
Solution Approach 1:
The differentiable emulator copy replaces computationally expensive iterative adjustments with efficient gradient-based optimization. By replicating the software's behavior in a differentiable framework, the system can use neural network optimizers that converge faster and with lower computational cost than traditional iterative slider adjustment methods.
Solution Approach 2:
The patent substitutes the mechanical iterative adjustment process with a neural network-based optimization system. Instead of manually or iteratively adjusting sliders through trial and error, the system uses gradient descent and backpropagation to automatically find optimal parameters, replacing the mechanical adjustment mechanism with a learning-based approach that reduces computational expense.
3Loss of time
If existing machine learning approaches are used for automatic tuning, then time consumption is reduced, but intuitive control and creative capabilities are lost
Solution Approach 1:
The patent makes the system dynamic by allowing users to intervene at any point during the automated tuning process. Users can adjust parameters, modify the target outcome, or combine automated suggestions with manual adjustments. This dynamic interaction maintains creative control while benefiting from time-efficient automated optimization, unlike static machine learning approaches that fully automate the process.
Solution Approach 2:
The system incorporates feedback loops where users can provide input to refine the automated tuning results. The translator enables bidirectional communication between the user interface and the differentiable emulator, allowing users to adjust parameters and immediately see the effect on the optimized output, thus maintaining intuitive control while leveraging automated time-saving capabilities.
4Productivity
If differentiable emulation is implemented, then computational efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the overall system into distinct modular components: the differentiable emulator, the translator, and the original non-differentiable software. Each component has a specific function and can be developed, trained, and maintained independently. This segmentation manages system complexity by breaking down the complex task of creating a differentiable replacement into manageable modules with clear interfaces.
Solution Approach 2:
The translator serves as an intermediary that manages the complexity of interfacing between the differentiable emulator and the original software. It handles parameter conversion and coordination, isolating the complexity of the differentiable-emulation interface from the user-facing original software, thus improving computational efficiency while containing system complexity within the translator layer.
Data Source
AI summary
Methods and systems are provided for differentiable emulation of non-differentiable image processing for adjustable and explainable non-destructive image and video editing. In embodiments described herein, an emulator is trained to predict corresponding data structures from non-differentiable image processing control parameters, the data structures being differentiable with respect to each control parameter of the non-differentiable image processing control parameters. A translator is trained to predict a predicted set of control parameters from the non-differentiable image processing control parameters. An updated image is generated by applying the predicted set of control parameters from the translator to an input image. The updated image is subsequently displayed with the predicted set of control parameters.