Digital Image Acceptability Detection Using Edge Density Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for automatically determining the acceptability of digital images for printing are inadequate, particularly in assessing sharpness, leading to unnecessary prints of low quality and increased costs due to reliance on visual inspection and insufficient differentiation of image quality.
Innovation Solution
A method that identifies important areas in an image, calculates salient acceptability features, and determines image acceptability based on these features to prevent unacceptable images from being printed, using edge detection algorithms like Canny's to assess edge density and noise levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection is used to detect unacceptable images, then operator judgment can identify poor quality images, but the process is time-consuming and costly
Solution Approach 1:
The patent replaces manual visual inspection with an automated computer-based system that uses image processing algorithms to detect sharpness and quality issues. The system automatically analyzes focus quality, edge sharpness, and other quality metrics without human intervention, thereby eliminating time loss while maintaining or improving detection accuracy.
Solution Approach 2:
The system enables images to self-assess their own quality through automated algorithms that evaluate sharpness, focus, and other quality parameters. Each image is independently analyzed by the computer system using predefined criteria, allowing rapid self-service quality determination without requiring operator time.
2Reliability
If all images are printed regardless of quality, then no images are lost, but wasteful printing of low quality images increases costs
Solution Approach 1:
The system performs preliminary quality assessment of images before they are printed. By evaluating sharpness, focus, and other quality parameters in advance, the system identifies unacceptable images and prevents them from being printed, thereby avoiding material waste while ensuring only quality images are output.
Solution Approach 2:
The system changes the decision parameter for printing from a binary all-or-nothing approach to a quality-based selective approach. By introducing quality thresholds and acceptability criteria, the system dynamically determines which images meet the required standards for printing, optimizing both resource utilization and output quality.
3Productivity
If simple edge detection is used to assess sharpness, then processing is fast, but accuracy in detecting poor focus is insufficient
Solution Approach 1:
The system applies different analysis methods to different regions of the image based on local quality requirements. Important areas such as the main subject receive more rigorous sharpness analysis using multiple algorithms and metrics, while less critical areas use simpler assessment methods, thereby maintaining high accuracy where needed while preserving overall processing efficiency.
Solution Approach 2:
The system combines multiple edge detection algorithms and quality assessment methods into a composite analysis framework. By integrating several detection techniques with different strengths, the system achieves superior sharpness detection accuracy that leverages the advantages of each individual method while compensating for their respective limitations.
4Measurement precision
If comprehensive quality analysis is performed on all images, then detection accuracy improves, but processing complexity and time increase
Solution Approach 1:
The system segments the quality analysis process into distinct stages and components, including preliminary screening, detailed sharpness analysis, and final acceptability determination. By dividing the comprehensive analysis into manageable segments, the system achieves high detection accuracy while keeping each processing stage relatively simple and efficient.
Data Source
AI summary
A method for automatically determining the acceptability of an input image for a predetermined output operation, such as printing, includes the steps of: (a) identifying one or more important areas in the input image; (b) calculating a salient acceptability feature for each of the important areas; (c) determining the acceptability of the input image from the salient acceptability features of the important areas; and (d) implementing the predetermined output operation based on the acceptability of the input image determined in step (c).