Saliency-Based Image Processing for Toner-Saving Printing
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Solution Overview
Problem
Existing image forming technologies inefficiently manage toner consumption, particularly in areas that contribute less to the aesthetic appeal, leading to unnecessary toner usage and potential print quality degradation.
Innovation Solution
An image forming apparatus and method that utilizes machine learning to estimate attention levels in image areas, adjusting toner usage based on saliency and semantic segmentation to reduce toner consumption in less attention-grabbing areas while maintaining print quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If toner consumption is reduced in defocused areas using distance information, then toner usage decreases, but aesthetic appeal deteriorates because out-of-focus elements contribute to visual composition
Solution Approach 1:
The patent applies different toner reduction strategies to different regions of the image based on their aesthetic importance. Instead of uniformly reducing toner in defocused areas, the system identifies and preserves toner usage in regions that contribute to aesthetic appeal (such as out-of-focus flowers in a garden scene) while reducing toner in regions that do not contribute significantly to the overall aesthetic composition.
2Loss of substance
If machine learning is used to estimate attention levels, then toner usage is optimized, but device complexity increases
Solution Approach 1:
The patent introduces an image processing apparatus as an intermediary between the image data and the printer. This intermediary contains a machine learning model that estimates attention levels in image regions, enabling intelligent toner distribution without requiring the printer itself to be complex. The processing apparatus handles the computational complexity while the printer performs the simplified printing task.
Data Source
AI summary
An image forming apparatus includes a printer, at least one memory storing a program, and at least one processor that, upon execution of the program is configured to acquire an attention level in an area of an image based on image data, the attention level being estimated through machine learning, perform image processing on the image data based on the estimated attention level so that an amount of recording material to be used to draw an area with a low attention level in the image is less than an amount of recording material to be used to draw an area with a high attention level in the image, and cause the printer to print the image on a printing medium based on the image data having been subjected to the image processing.