ISP Parameter Tuning Using Saliency Maps for Vision Accuracy
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Solution Overview
Problem
Existing methods for tuning image signal processors (ISPs) in digital cameras and computer vision tasks are inefficient and require expert knowledge or simplify the ISP, leading to suboptimal results due to lack of accurate correspondence between ISP parameters and task evaluation indicators.
Innovation Solution
An information processing apparatus that generates a saliency map based on a predetermined model to adjust ISP parameters, reflecting the importance of objects in an image, thereby optimizing the ISP for improved task processing accuracy without expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert knowledge is used to tune ISP parameters, then tuning accuracy is improved, but device complexity and time consumption increase
Solution Approach 1:
The system automatically generates saliency maps using a predetermined model to guide ISP parameter tuning, eliminating the need for expert intervention. The automated pipeline includes: (1) inputting sample images into the predetermined model, (2) automatically generating saliency maps that reflect model decision-making processes, (3) using these saliency maps to adjust ISP parameters, and (4) iteratively optimizing until task processing accuracy satisfies predetermined conditions.
Solution Approach 2:
The patent replaces the mechanical process of expert manual tuning with an automated computational system. Instead of relying on human experts to adjust parameters based on experience, the system uses algorithmic generation of saliency maps and automated parameter optimization, substituting human expertise with computational mechanisms.
2Ease of operation
If ISP is simplified for easier processing, then ease of operation is improved, but manufacturing precision and tuning accuracy deteriorate
Solution Approach 1:
The patent introduces saliency maps as an intermediary that bridges the gap between ISP parameters and task evaluation indicators. The saliency map serves as a mediator that translates model decision-making processes into actionable guidance for parameter tuning, enabling accurate tuning without simplifying the ISP structure.
Solution Approach 2:
The system implements a feedback loop where the predetermined model processes sample images through the ISP, generates saliency maps that reflect decision-making processes, and uses these maps to adjust parameters. This iterative feedback mechanism continues until the difference between task processing results and marked values satisfies predetermined conditions, ensuring high tuning accuracy.
3Productivity
If task evaluation indicator is used directly for tuning, then productivity is improved, but measurement precision deteriorates due to lack of accurate correspondence
Solution Approach 1:
The patent segments the image processing task by generating spatially-resolved saliency maps that identify important regions at different positions in the image. This segmentation allows the system to focus tuning efforts on specific regions that the model deems important, rather than treating the entire image uniformly, thereby improving both efficiency and precision.
Solution Approach 2:
The patent adds a new dimension to the tuning process by incorporating spatial information through saliency maps. Instead of tuning based solely on overall task evaluation indicators, the system introduces spatial awareness by generating maps that show the importance of different regions, enabling more precise and informed parameter adjustment.
Data Source
AI summary
The present application provides an information processing device and method, and a computer-readable storage medium. The information processing device comprises a processing circuit which is configured to: generate a saliency map of a sample image on the basis of a predetermined model which processes a task for the sample image, wherein the saliency map reflects the degree of attention to objects at different positions in the sample image when the predetermined model processes the task; and adjust, on the basis of the saliency map and the labeling area in the sample image, the parameters of the image signal processor which generates the sample image, such that the difference between the task processing result and the labeling value of the sample image meets a preset condition.