Image Cropping Preview for Faster High-Resolution Composition
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Solution Overview
Problem
Users are dissatisfied with the composition of shot images and need to manually crop them to improve the presentation of body images, which is time-consuming and inefficient.
Innovation Solution
An image processing method that automatically crops a current image based on a target composition image selected from composition preview images, using a compressed image with lower resolution to determine the cropping range, and applies image recognition and composition rules to ensure the desired composition is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually crop shot images to improve composition, then the presentation of body images becomes more reasonable, but the process consumes more time and reduces efficiency
Solution Approach 1:
The system performs automatic cropping by itself without requiring user intervention. The processor automatically determines the cropping range based on the compressed image and composition rules, then applies this to the current image, making the system serve itself rather than requiring manual user operation.
Solution Approach 2:
The system performs preliminary cropping on the compressed image to generate composition preview images before processing the full-resolution current image. This preliminary action on a smaller, lower-resolution image allows the system to determine the optimal cropping range quickly, which is then applied to the high-resolution image to achieve both speed and quality.
2Manufacturing precision
If users manually crop images to adjust composition, then the body image presentation improves, but the operation becomes more complex and less convenient
Solution Approach 1:
The system automatically determines the cropping range and performs the cropping operation without requiring user interaction. The processor analyzes the compressed image, applies composition rules, and executes the cropping autonomously, making the system self-sufficient and eliminating the need for manual user operations.
Solution Approach 2:
The system creates composition preview images by cropping the compressed image, which serves as a copy or representation of the full-resolution image. Users can select from these preview copies to determine the final cropping range, simplifying the operation by working with smaller, easier-to-preview images before applying to the original.
3Manufacturing precision
If the system processes high-resolution images for cropping preview, then the composition accuracy improves, but the system memory consumption increases
Solution Approach 1:
The processing task is segmented into two stages: first processing the compressed low-resolution image to determine the cropping range, then applying this range to the high-resolution current image. This segmentation allows the computationally intensive preview generation to occur on smaller data, reducing memory usage while maintaining final output quality.
Solution Approach 2:
The system performs preliminary cropping operations on the compressed image before processing the full-resolution image. This preliminary action on lower-resolution data reduces the memory and computational resources required for the initial analysis, while the final high-quality output is achieved by applying the predetermined cropping range to the original high-resolution image.
Data Source
AI summary
An image processing method, the method includes: obtaining a compressed image of a current image, where resolution of the current image is greater than that of the compressed image; cropping out at least one composition preview image based on the compressed image; and in response to selecting a target composition image from said at least one composition preview image displayed, cropping the current image based on a cropping range indicated by the target composition image.