Automated Color Profile Contour Artifact Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for identifying and reducing contour artifacts in image processing are costly, time-consuming, and prone to errors, as they rely on manual evaluation of test images by human experts, which may not fully represent potential issues in customer images.
Innovation Solution
An automated system and method that samples straight lines between reference points in an input color space, converts these points to an output color space, identifies discontinuities, and generates test images to validate candidate reference point pairs for contour artifact detection, allowing for the development of modified color profiles to reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of test images by human experts is used, then contour artifacts can be identified, but the process becomes costly, time-consuming, and error-prone
Solution Approach 1:
The patent replaces the manual mechanical evaluation process with an automated computational system. The system uses a model of human visual perception to automatically detect contour artifacts by analyzing color gradients and identifying non-smooth transitions, eliminating the need for human experts to manually evaluate test images while maintaining detection accuracy.
Solution Approach 2:
The system enables self-service by allowing the color profile development process to automatically identify and flag problematic color profiles without human intervention. The automated detection system evaluates color gradients and generates reports, enabling the color development team to focus only on profiles that require manual review, thus reducing overall evaluation time and costs.
2Reliability
If a large library of test images is used, then more potential contour artifact issues may be detected, but the process remains costly and time-consuming
Solution Approach 1:
The patent extracts the essential detection logic from the manual evaluation process and implements it as an automated algorithm. By taking out the core function of contour artifact detection and encoding it in software, the system can evaluate any number of test images rapidly without the proportional increase in time and cost associated with manual human evaluation.
Solution Approach 2:
The system changes the parameter of evaluation speed by using computational algorithms that can process color gradient data instantaneously. This allows the system to maintain high reliability by evaluating comprehensive test image libraries while achieving high productivity through automated processing that does not suffer from the diminishing returns of manual evaluation.
3Manufacturing precision
If conventional color profiles are used for conversion, then color image quality may be compromised, but developing modified profiles is time-consuming
Solution Approach 1:
The patent applies preliminary action by automatically identifying problematic color profiles before they are fully developed and deployed. The system evaluates color gradients during the profile development process and flags profiles that are likely to produce contour artifacts, allowing developers to make corrections early in the process rather than discovering issues after deployment, thus reducing overall development time while ensuring quality.
4Productivity
If automated detection systems are implemented, then evaluation time and costs are reduced, but the system complexity increases
Solution Approach 1:
The patent uses an intermediary approach by implementing a software-based detection system that acts as a mediator between the color profile data and the evaluation process. This intermediary system translates complex color gradient analysis into simple pass/fail determinations, achieving high productivity through automation while keeping the system complexity manageable by using well-established image processing algorithms and color space transformations.
Data Source
AI summary
A system and method provide for automated evaluation of reference point pairs. For each of a set of reference point pairs in an input color space, a straight line connecting the reference points is sampled to generate a set of sampled points. Each of the set of sampled points in the input color space is converted to a sampled point in an output color space. For each of a set of color separations in the output color space, discontinuities are identified, based on the set of sampled points in the output color space. Candidate reference point pairs are identified in the set of reference point pairs for which at least one discontinuity is identified. The candidate reference point pairs can be validated by printing test sweeps, which are each derived from a respective set of sampled points in the output color space, and identifying contour artifacts in the printed test sweeps.