Semantic Image Classification for Printing Enhancement
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Solution Overview
Problem
Office workers face challenges in classifying and enhancing images captured with digital cameras for high-quality printing, as existing systems can only perform basic image processing based on low-level features, failing to tailor enhancements according to the semantic type of the image.
Innovation Solution
A printing system that classifies images into semantic categories such as whiteboard, business card, document, and slide images, applying tailored enhancement processing and selecting print options based on the classification to improve the printed output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If basic image processing is applied based on low-level features, then processing time is reduced and system complexity is minimized, but image quality and semantic accuracy deteriorate
Solution Approach 1:
The patent segments the image processing task into multiple stages: low-level feature extraction (contrast, darkness, color), mid-level semantic classification (whiteboard, business card, document, slide, regular image), and high-level enhancement processing. Each stage handles specific aspects independently, allowing the system to achieve high image quality through specialized processing for each semantic category without overwhelming system complexity.
Solution Approach 2:
The patent applies different enhancement processing methods tailored to each semantic category of image. For example, whiteboard images receive specific enhancement to improve text readability, while business card images receive different processing to preserve contact information quality. This local quality approach ensures optimal image quality for each type without requiring a single complex universal processor.
2Manufacturing precision
If manual classification and enhancement is performed by office workers, then image quality can be optimized, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements automatic semantic classification and enhancement processing that operates without user intervention. The system automatically detects the semantic category of captured images (whiteboard, business card, document, slide, or regular image) and applies appropriate enhancement processing tailored to each category. This self-service capability eliminates the need for office workers to manually classify and enhance images, saving time while maintaining high image quality.
Solution Approach 2:
The system incorporates automatic feedback loops where the classified semantic category directly determines the enhancement processing applied. The classification result feeds into the enhancement module, which automatically selects and applies the appropriate processing parameters and methods, eliminating manual decision-making time while ensuring optimal quality for each image type.
3Manufacturing precision
If generic image processing is applied to all images, then system simplicity is maintained, but image quality and readability for specific types deteriorate
Solution Approach 1:
The patent implements local quality by applying different enhancement processing methods to different semantic categories of images. Whiteboard images receive processing optimized for text readability, business card images receive processing that preserves contact information clarity, document images receive appropriate enhancement, and slides receive tailored processing. This ensures high readability for each specific image type rather than applying a generic one-size-fits-all approach.
Solution Approach 2:
The system changes processing parameters based on the semantic category of the image. Different enhancement parameters (contrast levels, sharpening intensity, color adjustments, noise reduction settings) are applied according to the classified image type. This parameter adaptation allows the system to achieve high readability for each specific image type while maintaining a unified processing framework.
Data Source
AI summary
A printing system enables the printing of enhanced documents using a semantic classification scheme. A printing system receives an image to be printed. The system classifies the image according to the semantic classification scheme and, based on this classification, performs enhancement processing on the image. Depending on the desired application, the printing system may recognize and classify any number of image types and may then perform various enhancement processing functions on the image, where the type of enhancement processing performed is based on the classification of the image.