Grouped ISP Sub-task Simulation for Tuning Bottlenecks
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Solution Overview
Problem
Current image signal processing (ISP) simulators are slow and inefficient, requiring the simulation of all sub-tasks from start to end for each tuning, which is time-consuming and often results in unwanted side effects due to neglecting dependencies between sub-tasks.
Innovation Solution
A method that defines groups of sub-tasks within the ISP pipeline, allowing for iterative simulation and parameter tuning of specific groups, reducing the need to simulate the entire pipeline for each adjustment and considering dependent sub-tasks, thereby optimizing image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire ISP pipeline is simulated from start to end for each parameter tuning, then the complete image processing result can be evaluated, but the simulation time becomes excessively long
Solution Approach 1:
The ISP pipeline is divided into multiple sub-tasks (e.g., demosaicing, denoising, color correction, tone mapping). The patent enables selective simulation of only the sub-tasks that need to be tuned, rather than simulating the entire pipeline from start to end for each parameter adjustment. This segmentation allows efficient parameter optimization by focusing computation only on relevant portions of the pipeline.
Solution Approach 2:
The patent pre-calculates and stores intermediate results from sub-tasks that do not need to be tuned. By caching these intermediate outputs, the system avoids re-simulating unchanged sub-tasks during parameter optimization of other sub-tasks, significantly reducing the overall simulation time while maintaining evaluation accuracy.
2Productivity
If only a single sub-task is simulated for tuning, then the simulation speed increases, but the results become unpredictable due to neglected dependencies
Solution Approach 1:
The patent merges the simulation of dependent sub-tasks into a coordinated process. When tuning parameters of one sub-task, the system automatically includes simulation of sub-tasks that depend on it, ensuring that all causal relationships are captured. This merging approach maintains result predictability while avoiding unnecessary simulation of independent sub-tasks.
Solution Approach 2:
The patent implements a dependency-aware feedback mechanism that tracks which sub-tasks affect which others. When parameters of a sub-task are modified, the system uses feedback information about dependency relationships to automatically determine which subsequent sub-tasks must be re-simulated, ensuring predictable and reliable tuning results without redundant computation.
3Loss of time
If the ISP simulation stops after a tuned sub-task, then the simulation time is reduced, but unwanted side effects from later sub-tasks are neglected
Solution Approach 1:
The patent employs a dynamic simulation approach where the simulation boundary automatically adjusts based on the tuned sub-task and its dependencies. Instead of fixed stop points, the system dynamically determines the appropriate simulation endpoint by analyzing dependency graphs, ensuring that all sub-tasks affected by parameter changes are included while excluding independent sub-tasks that would produce unwanted side effects.
Data Source
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AI summary
Method and device for optimization an image signal processing, wherein the image signal processing comprises a plurality of individual sub-tasks performed in a predetermined order, wherein the method comprises the steps of: defining a first group of sub-tasks having a starting point and an end point in the predetermined order and including at least two or more sub-tasks; performing the sub-tasks in the predetermined order until the starting point of the first group; performing the sub-tasks of the first group with a predetermined first set of parameters until the end point of the first group; modifying the predetermined first set of parameters; and performing the sub-tasks of the first group with the modified first set of parameters until the end point of the first group.