Variable Image Quality Segmentation for Mobile Storage
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Solution Overview
Problem
The increasing demand for high-quality images from mobile devices leads to inefficient data storage due to uniform compression of image data, which does not effectively prioritize visually important elements.
Innovation Solution
A system and method that dynamically manage image quality by segmenting image data into high and low quality portions, using techniques such as fixed areas, visual element recognition, and user input to apply varying levels of compression, prioritizing important elements like faces over background scenery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If uniform compression is applied to the entire image data, then the file size is reduced, but the image quality of important areas is compromised
Solution Approach 1:
The image data is divided into multiple regions with different compression levels. A high-quality region (containing important visual elements like faces) is segmented from the rest of the image, allowing differential compression to be applied where high quality is preserved in important areas while lower quality is acceptable in less important areas.
Solution Approach 2:
Different compression qualities are applied to different regions of the image based on their visual importance. The high-quality region receives minimal or no compression to preserve detail, while other regions receive higher compression, optimizing the balance between file size and perceived image quality.
2Manufacturing precision
If high quality is maintained for the entire image, then image fidelity is preserved, but memory storage capacity is exceeded
Solution Approach 1:
The image is segmented into a high-quality region and other regions, allowing the overall file size to be reduced by compressing non-critical areas while maintaining high quality in important areas, thus fitting memory constraints.
Solution Approach 2:
High image quality is maintained locally in the high-quality region (e.g., face area) while other regions use lower quality compression, achieving an efficient use of memory storage that prioritizes visually important content.
3Ease of manufacture
If uniform compression is used, then processing is simple, but visually important elements do not receive priority treatment
Solution Approach 1:
The image processing system segments the image to identify a high-quality region containing visually important elements. This segmentation enables priority treatment of important areas through differential compression while maintaining relatively simple processing compared to more complex adaptive methods.
Data Source
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AI summary
Image data for a digital photograph is segmented into a first portion and a second portion. The image data of the second portion is processed to have a quality lower than the image data of the first portion. An image file for the scene is stored where the image file contains image data corresponding to the first and second portions so that the image file has a high quality portion and a low quality portion.