Dynamic Image Resolution Segmentation for Mobile Data Optimization
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Solution Overview
Problem
The challenge lies in balancing image quality and file size, particularly in mobile devices with limited data availability, where high-resolution images require more memory and bandwidth, leading to slower loading times and increased data consumption.
Innovation Solution
A computer-implemented method that identifies contextually relevant portions of an image, creates boundaries to define these areas, and generates an altered version with higher resolution in relevant portions and lower resolution in less important areas, optimizing file size and loading time while maintaining quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution images are used, then image quality is improved, but file size increases and loading time increases
Solution Approach 1:
The image is divided into multiple regions of interest (ROIs) and non-ROI portions. The ROIs are identified based on user context data such as user profile, current activity, and preferences. Only the ROI portions are transmitted at high resolution, while non-ROI portions are transmitted at lower resolution or omitted entirely, reducing overall file size and loading time.
Solution Approach 2:
Different resolution qualities are applied to different portions of the image based on their importance. The ROI portions receive high-resolution treatment to maintain image quality where it matters most, while non-ROI portions use lower resolution to reduce data transmission requirements.
2Manufacturing precision
If high-resolution images are used, then image quality is improved, but data consumption increases
Solution Approach 1:
The image data is segmented into ROI and non-ROI portions. By identifying and transmitting only the essential ROI portions at high resolution, the overall data consumption is reduced while maintaining perceptual image quality for mobile users with limited data availability.
Solution Approach 2:
The essential information (ROI portions) is extracted from the full image and transmitted separately at high resolution. The non-essential portions are either transmitted at lower resolution or excluded, effectively removing unnecessary data from the transmission stream.
3Quantity of substance
If image compression is increased, then file size is reduced, but image sharpness and detail are lost
Solution Approach 1:
Different compression levels are applied to different image portions. The ROI portions use minimal or no compression to preserve sharpness and detail, while non-ROI portions use higher compression to reduce file size, achieving an optimal balance between quality and size.
Data Source
AI summary
Systems and methods for dynamic modification of image resolution are disclosed. In embodiments, a method comprises: identifying, by the computing device, one or more contextually relevant portions of a digital image based on user context data; creating, by the computing device, boundaries that define the one or more contextually relevant portions of the digital image; ranking, by the computing device, the one or more contextually relevant portions and one or more remaining portions of the digital image; and generating, by the computing device, an altered version of the digital image, wherein the altered version comprises one or more contextually relevant portions at a higher resolution than the one or more remaining portions.