Image Region Pixel Density Processing via Layout Segmentation
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Solution Overview
Problem
Existing techniques for creating high-definition printed materials and image displays often require uniform pixel count enhancement across entire images, which is inefficient as it wastes resources on regions that do not need high pixel density, such as hidden or solid color areas.
Innovation Solution
An information processing method that analyzes image layout and applies pixel count changing processing only to specific regions of an image based on layout information, determining which areas require upsampling or downsampling to optimize pixel density without unnecessary processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If uniform pixel count enhancement is applied to entire images, then image quality is improved, but processing time and power consumption increase
Solution Approach 1:
The image is divided into multiple regions based on layout information, and pixel count enhancement is applied selectively to specific regions rather than uniformly to the entire image. This segmentation allows processing to focus only on areas that require high pixel density, thereby reducing overall processing time while maintaining image quality in critical areas.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on their specific requirements. Regions that require high pixel density (such as image content areas) receive pixel count enhancement, while regions that do not require enhancement (such as solid color areas or hidden regions) are processed differently or skipped entirely, optimizing both quality and efficiency.
2Manufacturing precision
If uniform pixel count enhancement is applied to entire images, then image quality is improved, but power consumption increases
Solution Approach 1:
The image processing task is segmented into region-specific operations based on layout analysis. By identifying and isolating regions that require pixel count enhancement from those that do not, the system reduces the total computational workload and associated power consumption while maintaining image quality in the critical enhanced regions.
Solution Approach 2:
Power consumption is optimized by applying pixel count enhancement only locally to regions where it is necessary for image quality, rather than uniformly across the entire image. This local quality approach ensures that computational resources and energy are allocated efficiently to areas that truly benefit from enhancement.
3Manufacturing precision
If pixel count enhancement is applied to all regions, then image quality is improved, but processing efficiency decreases
Solution Approach 1:
The image is segmented into multiple regions based on layout information, allowing the processing system to identify and prioritize regions that require pixel count enhancement. This segmentation enables parallel or sequential processing of only the necessary regions, significantly improving processing efficiency compared to uniform enhancement of the entire image.
Solution Approach 2:
Processing efficiency is enhanced by applying pixel count enhancement only to regions where it is needed for image quality, rather than uniformly to all regions. This local quality approach reduces the total number of processing operations required, thereby improving overall processing efficiency while maintaining quality in critical areas.
4Productivity
If selective region processing is applied, then processing efficiency is improved, but image quality may be compromised in non-processed regions
Solution Approach 1:
The image is segmented into regions based on layout information that identifies areas requiring high pixel density. By strategically selecting which regions to enhance based on their importance and visual significance, the system ensures that image quality is maintained in critical areas while improving overall processing efficiency through selective processing.
Solution Approach 2:
Different processing approaches are applied to different regions based on their specific quality requirements. Regions that are visually important or contain detailed content receive pixel count enhancement to maintain or improve quality, while regions with solid colors or minimal detail may use different processing strategies, optimizing both efficiency and quality outcomes.
Data Source
AI summary
An information processing method obtains image data representing an image laid out on a page, and sets, in the image, a target region on which to perform predetermined processing to change a pixel count, based on layout information pertaining to a layout of the image on the page. Based on this setting, the predetermined processing is performed on the target region in the image.