Automatic Image Processing via Semantic Pixel Grouping
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Solution Overview
Problem
Image capture devices, such as smartphones, face challenges in capturing images with colors and contrast as perceived by the human eye, and existing image processing techniques may not be readily available or are too time-consuming for users to implement effectively.
Innovation Solution
A computer-implemented method for automatically processing images by detecting pixel groups, associating them with semantic data, and applying adjustment parameters based on reference images to improve image quality, which includes identifying suitable reference images and applying post-processing techniques such as normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual image processing techniques are used, then image quality can be improved, but the process is too time-consuming for users to implement effectively
Solution Approach 1:
The system performs image processing automatically without requiring user intervention. The computing device autonomously detects pixel groups, identifies reference images, determines adjustment parameters, and applies processing operations, making the system self-service oriented and eliminating the time-consuming manual process.
Solution Approach 2:
The system performs preliminary actions by pre-identifying reference images and pre-determining adjustment parameters based on the candidate image's pixel groups and semantic data before final processing. This preparation enables efficient automatic processing without time-consuming manual steps.
2Manufacturing precision
If advanced image processing techniques are applied, then image quality improves, but the complexity of the processing system increases
Solution Approach 1:
The processing system is segmented into distinct functional modules: pixel group detection, semantic data association, reference image identification, adjustment parameter determination, and image processing application. This segmentation makes the complex system more manageable and easier to implement while maintaining high image quality.
Solution Approach 2:
Semantic data acts as an intermediary between the candidate image pixel groups and the adjustment parameters. The system associates semantic data with pixel groups, uses this semantic information to identify reference images, and then determines appropriate adjustment parameters, simplifying the overall processing logic.
3Ease of operation
If automatic processing is implemented, then ease of operation improves, but the system requires sophisticated algorithms for pixel group detection and reference image identification
Solution Approach 1:
The system handles all processing operations automatically without requiring user input or manual intervention. Users simply provide the candidate image, and the system autonomously performs pixel group detection, reference image identification, parameter determination, and processing application, making it extremely easy to operate despite the sophisticated algorithms involved.
Data Source
AI summary
Systems and methods for the processing of images are provided. In particular, a candidate image can be obtained for processing. The candidate image can have one or more associated image categorization parameters. One or more pixel groups can then be detected in the candidate image and the one or more pixel groups can be associated with semantic data. At least one reference image can then be identified based at least in part on the semantic data of the one or more pixel groups. Once the at least one reference image has been identified, a plurality of adjustment parameters can be determined. One or more pixel groups from the candidate image can then be processed to generate a processed image based at least in part on the plurality of adjustment parameters.