Image Sensor Region Conversion for Resolution and Power Balance
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Solution Overview
Problem
Existing electronic devices with image sensors face challenges in generating image data with improved resolution and low power consumption, as they often require complex circuits for high-resolution areas and simple circuits for low-resolution areas, leading to inefficient power usage and image quality variations.
Innovation Solution
An electronic device with an image sensor and processor that performs directional interpolation and upscale operations on specific areas of image data, combining the results through alpha blending to generate enhanced image data while adaptively selecting between high-power and low-power conversion methods based on image data location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex conversion circuits are used for high-resolution areas, then image quality is improved, but power consumption increases
Solution Approach 1:
The patent applies different conversion methods to different regions of the image sensor. High-resolution areas use complex directional interpolation conversion, while low-resolution areas use simple upscale conversion. This local differentiation allows the system to maintain high image quality where needed while reducing power consumption in areas where high resolution is not required.
Solution Approach 2:
The image sensor output is divided into multiple regions with different resolution requirements. The processor segments the image data and applies appropriate conversion methods to each segment, rather than uniformly applying complex conversion to the entire image. This segmentation strategy resolves the contradiction by localizing high-power operations only to necessary areas.
2Use of energy by moving object
If simple conversion circuits are used for low-resolution areas, then power consumption is reduced, but image quality deteriorates
Solution Approach 1:
The patent recognizes that different regions require different quality levels and applies conversion methods accordingly. Low-resolution areas use simple upscale conversion to maintain acceptable quality while saving power, while high-resolution areas receive complex directional interpolation to ensure high quality where needed.
Solution Approach 2:
The image is segmented into regions based on resolution requirements. By identifying which areas need high resolution and which can accept lower resolution, the system applies appropriate conversion methods to each segment, preventing overall quality deterioration while managing power consumption.
3Manufacturing precision
If uniform high-resolution conversion is applied to all areas, then image quality is improved, but device complexity increases
Solution Approach 1:
Instead of uniformly applying complex high-resolution conversion to the entire image, the patent applies complex conversion only to specific high-resolution areas that require it. Low-resolution areas use simpler conversion methods, reducing overall device complexity while maintaining necessary image quality.
4Device complexity
If uniform low-resolution conversion is applied to all areas, then device complexity is reduced, but image quality deteriorates
Solution Approach 1:
The patent applies simple low-resolution conversion only to areas where it is sufficient, while applying complex high-resolution conversion to areas where quality is critical. This localized approach maintains acceptable device complexity while preventing overall image quality deterioration.
Data Source
AI summary
An electronic device includes an image sensor configured to capture a target to generate first image data, and a processor configured to perform directional interpolation on a first area of the first image data to generate first partial image data, perform upscale on a second area of the first image data to generate second partial image data, and combine the first partial image data and the second partial image data to generate second image data.


