Image Processing Device Selecting Images by Evaluating Multiple Indices
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Solution Overview
Problem
Existing image processing devices select images based solely on predetermined criteria, potentially overlooking images that excel in other evaluation indices, resulting in suboptimal selections.
Innovation Solution
An image processing device that evaluates images using multiple criteria, applies specific image processing techniques to enhance evaluation values, and selects images based on improved evaluation scores, incorporating correlation data to determine effective processing types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image processing is applied to improve evaluation values, then image quality is improved, but processing time and complexity increase
Solution Approach 1:
The system performs preliminary evaluation of multiple images using multiple evaluation indices before selection. By pre-calculating evaluation values for all candidate images across multiple indices, the system identifies which images require processing and what types of processing would be most beneficial, avoiding unnecessary processing of already high-quality images and reducing overall processing time.
Solution Approach 2:
The system dynamically adjusts processing parameters based on evaluation index results. For each image, it identifies specific evaluation indices that need improvement and applies targeted image processing techniques to enhance only those aspects, rather than applying uniform processing to all images. This selective approach improves efficiency and reduces processing time.
2Measurement precision
If multiple evaluation indices are used to select images, then selection accuracy is improved, but device complexity increases
Solution Approach 1:
The evaluation system is segmented into multiple independent evaluation indices, each assessing a specific aspect of image quality (e.g., composition, color, sharpness). This modular approach allows the system to evaluate images comprehensively while maintaining manageable complexity, as each index can be calculated and weighted independently rather than requiring a single complex evaluation function.
Solution Approach 2:
The image processing device incorporates multiple evaluation indices and processing techniques that can be universally applied to different types of images. The system uses a standardized framework that handles various image types and quality aspects through common evaluation and processing mechanisms, reducing the need for image-specific complex logic while maintaining high selection accuracy.
3Manufacturing precision
If image processing is applied to all images, then overall quality improves, but processing efficiency decreases
Solution Approach 1:
The system applies image processing locally and selectively based on individual image characteristics and evaluation index results. Instead of uniform processing, it identifies specific images that need improvement and applies targeted processing techniques to enhance only those aspects that will most benefit the overall selection quality, thereby maintaining high efficiency while improving overall image quality.
Solution Approach 2:
The system performs partial processing on selected images rather than exhaustive processing on all images. By using multiple evaluation indices to identify only those images that would benefit from processing, and applying processing only to those specific cases, the system achieves sufficient quality improvement without the excessive processing overhead of treating all images uniformly.
Data Source
AI summary
An image processing device includes: an evaluation unit that evaluates a plurality of images by designating predetermined image characteristics as an evaluation index; an image processing unit that executes image processing, which will affect the evaluation index, on at least one image among the plurality of images; and an image selection unit that selects an image with superiority in an evaluation value calculated in correspondence to the evaluation index, among the plurality of images evaluated by the evaluation unit by factoring in application of the image processing by the image processing unit.