Adaptive Image Zoom Using Key Pixel Density Segmentation
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Solution Overview
Problem
Existing image zooming methods result in degradation due to uniform scaling, leading to increased computation and time, and fail to consider the image's characteristics.
Innovation Solution
The method segments the image based on the density of important pixels, determines a priority order for zooming, and applies adaptive zoom levels to each segment, using AI and bilinear zoom techniques to enhance quality and reduce computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform zoom scaling is applied to the entire image, then the zoom operation is simple to implement, but image quality degrades and computation time increases
Solution Approach 1:
The image is divided into multiple segments based on key pixel density, allowing different zoom operations to be applied to different regions. This segmentation enables selective processing where high-quality AI zoom is applied only to important regions containing key pixels, while non-critical regions use simpler bilinear zoom, thus improving overall image quality while reducing computation time
Solution Approach 2:
Different zoom algorithms are applied to different segments based on their local characteristics (key pixel density). Important regions with high key pixel density receive AI-based zoom for superior quality, while less important regions use bilinear interpolation. This local differentiation optimizes the balance between image quality and computational efficiency
2Ease of manufacture
If uniform zoom scaling is applied to the entire image, then the implementation is straightforward, but computation time and resources increase
Solution Approach 1:
The image is divided into multiple segments based on key pixel density, allowing different zoom operations to be applied to different regions. This segmentation enables selective processing where high-quality AI zoom is applied only to important regions containing key pixels, while non-critical regions use simpler bilinear zoom, thus improving overall image quality while reducing computation time
Solution Approach 2:
Instead of applying computationally intensive AI zoom to the entire image, the method applies advanced processing only to segments containing key pixels (partial action). The remaining segments use faster bilinear interpolation, reducing overall computation time while maintaining quality where it matters most
3Ease of operation
If every area in the image is processed with equal importance, then the processing is uniform and simple, but the result does not consider image characteristics
Solution Approach 1:
Different zoom algorithms are applied to different segments based on their local characteristics (key pixel density). Important regions with high key pixel density receive AI-based zoom for superior quality, while less important regions use bilinear interpolation. This local differentiation optimizes the balance between image quality and computational efficiency
Solution Approach 2:
The zoom processing is made dynamic and adaptive rather than static and uniform. The system automatically identifies key pixels, determines their density in different segments, and dynamically selects the appropriate zoom algorithm for each segment. This adaptability ensures that image characteristics are considered and preserved in the zoomed output
Data Source
AI summary
A method for applying adaptive zoom on an image is disclosed. The method includes detecting at least one input to perform zoom on the image, determining key pixels in the image, obtaining a plurality of segments by determining sizes of each of the plurality of the segments based on a number of the key pixels in each of the plurality of the segments, determining a priority order for zooming the plurality of the segments based on a density of the key pixels in each of the plurality of the segments, determining a zoom level to be applied on each of the plurality of the segments based on the density of the key pixels in each of the plurality of the segments, and adaptively zooming each of the plurality of the segments based on the zoom level and the priority order.