Image Processing Apparatus Halftone Dot Detection Ink-Jet Classification
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Solution Overview
Problem
Existing image processing methods struggle to accurately classify and process ink-jet printing documents due to variations in resolution, color materials, and output papers, often resulting in image deterioration when inappropriate modes are selected.
Innovation Solution
An image processing apparatus that includes a discrimination section capable of detecting halftone dot areas in input image data, using threshold values to classify the type of image data into multiple types, allowing for precise detection of halftone dot areas in both ink-jet printed and electrophotography output documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional single-mode processing is used for ink-jet printing documents, then device complexity is reduced, but image quality deteriorates due to inability to handle various resolutions and color materials appropriately
Solution Approach 1:
The patent segments ink-jet printing documents into multiple categories based on resolution (low, middle, high) and color material characteristics. By dividing the processing into distinct modes corresponding to different document types, the system can apply appropriate processing parameters for each category, thereby improving image quality without requiring a single overly complex universal processor
Solution Approach 2:
The patent implements dynamic mode selection that automatically adjusts processing parameters based on detected document characteristics. The system dynamically switches between different processing modes (text, halftone, picture, ink-jet specific modes) according to the input document's resolution and color properties, enabling adaptive optimization of image quality
2Ease of operation
If automatic document type judgment is implemented, then ease of operation improves, but measurement precision deteriorates due to difficulty in recognizing all ink-jet printing document variations
Solution Approach 1:
The patent segments the document recognition process into multiple detection stages, first identifying broad categories (text, halftone, picture) and then further classifying ink-jet documents by resolution level. This segmented approach improves recognition accuracy by breaking down the complex classification task into manageable steps
Solution Approach 2:
The patent uses multiple detection parameters including color reproduction area characteristics, texture analysis, and resolution measurements to distinguish between different document types. By changing and combining multiple detection parameters, the system achieves higher recognition accuracy for diverse ink-jet printing documents while maintaining automatic operation
3Measurement precision
If high resolution scanning is used for all ink-jet documents, then measurement precision improves, but loss of time increases due to inability to read individual dots in high resolution output
Solution Approach 1:
The patent implements dynamic resolution selection that automatically adjusts the scanning resolution based on the detected document type and original output resolution. For high-resolution ink-jet output documents, the system dynamically selects appropriate lower scanning resolutions, avoiding unnecessary time consumption while maintaining adequate reading quality
Solution Approach 2:
The patent changes the scanning resolution parameter adaptively based on document characteristics. By detecting whether the input document is high-resolution ink-jet output and adjusting the scanning resolution accordingly, the system optimizes the balance between reading accuracy and processing time
Data Source
AI summary
An image processing apparatus includes an automatic document type discrimination section for estimating a type of input image data. The automatic document type discrimination section is capable of detecting whether each of plural types of halftone dot areas exists or not in the image data, and the automatic document type discrimination section estimates the type of the image data. Therefore, the image processing apparatus can exactly estimate the type of the image.