Feature Extraction for Scanned Image Gamut Optimization
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Solution Overview
Problem
Imaging devices often fail to accurately reproduce bright colors like highlighting marks during scanning and printing due to differences between the color gamut of the scanner and printer, leading to user frustration as manual adjustments require expertise and may result in incorrect or missing marks.
Innovation Solution
The implementation of feature extraction processes that convert scanned images into a perceptually uniform color space, segment hues, and apply a classification model to identify and optimize highlighting marks, ensuring they fall within the printer's color gamut for accurate reproduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of image quality settings is performed to accurately reproduce highlighting marks, then reproduction accuracy of bright colors is improved, but user expertise is required and operation becomes complex
Solution Approach 1:
The system automatically performs feature extraction, gamut mapping, and image optimization without requiring user intervention. The processor autonomously identifies highlighting marks, determines their colors, checks printer gamut compatibility, and adjusts reproduction parameters, enabling the device to serve itself rather than requiring expert user operation.
Solution Approach 2:
The system automatically changes image quality parameters including color space conversion, gamut mapping adjustments, and reproduction settings based on detected highlighting marks. These parameter modifications occur transparently during processing to ensure accurate reproduction without requiring manual user adjustment.
2Ease of operation
If feature extraction and classification processes are applied to automatically identify and optimize highlighting marks, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The processing system segments the scanned image into different feature categories including highlighting marks, text, and images. By dividing the image analysis into distinct segments with specific feature extraction processes for each type, the system manages complexity through organized modular processing rather than attempting to handle all pixels uniformly.
Solution Approach 2:
The system introduces intermediate processing steps including feature extraction, classification, and gamut mapping as mediators between scanning and printing. These intermediary processes translate and adapt image data between different color spaces and device capabilities, managing the complexity of direct device-to-device translation.
3Measurement precision
If the scanner's color gamut is used to detect highlighting marks, then detection capability is improved, but printer reproduction capability deteriorates due to gamut mismatch
Solution Approach 1:
The system performs preliminary gamut mapping and color space conversion during the scanning phase, before the actual printing occurs. By pre-processing the color data to account for printer gamut limitations, the system ensures that highlighting marks are reproduced accurately without requiring post-processing adjustments.
Solution Approach 2:
The system introduces color space conversion and gamut mapping as intermediary processes between the scanner's detection capability and the printer's reproduction capability. This intermediary layer translates colors from the scanner's wider gamut into the printer's reproducible gamut while preserving the appearance and intent of the original highlighting marks.
Data Source
AI summary
In some examples, an imaging device can include a processing resource and a memory resource storing instructions to cause the processing resource to perform a feature extraction process to extract a gamut-based feature included in a plurality of pixels of a scanned image to determine whether the scanned image includes a particular marking, apply a classification model to the scanned image, and optimize the scanned image based on the classification of the scanned image.