Automatic Image Cropping via Color Variation Scanning
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Solution Overview
Problem
Existing image cropping methods require manual trial and error, leading to inaccurate results and are time-consuming, especially when dealing with large groups of images like video files, as they lack an efficient automatic determination of crop areas.
Innovation Solution
A computer-implemented method that scans images from side to side to determine the degree of color variation among pixels, comparing these values to threshold values to automatically determine crop areas, allowing for accurate and speedy cropping of images or video files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual cropping is performed to remove letterbox areas, then the user can control the cropping areas, but the process requires numerous instances of trial and error and does not guarantee accurate results
Solution Approach 1:
The system automatically detects letterbox areas and determines crop values without requiring user intervention or manual trial and error. The automatic cropping feature analyzes the image data itself to identify and remove letterbox regions, making the system self-sufficient for this task.
Solution Approach 2:
The system changes the parameter being measured from manual user estimation to automatic color variation analysis. By scanning images and calculating color variation metrics, the system objectively determines crop boundaries based on actual image content rather than user guesswork.
2Productivity
If cropping is performed for a large group of images such as a video file, then comprehensive cropping can be achieved, but the process is time-consuming
Solution Approach 1:
The system performs preliminary analysis by scanning a subset of frames from video files to determine representative crop values. By analyzing only certain frames rather than every frame, the system prepares crop information in advance, significantly reducing the time required for processing large numbers of images while maintaining accuracy.
Solution Approach 2:
The system processes a partial subset of frames from video files rather than analyzing every single frame. This partial action approach provides sufficient information to determine accurate crop values for the entire video while dramatically reducing processing time compared to exhaustive analysis.
3Ease of operation
If automatic cropping feature is enabled to process multiple images, then efficiency is improved, but the system must select and process images systematically
Solution Approach 1:
The system provides a universal automatic cropping feature that can process individual images or large groups of images including video files. The same core algorithm handles both single-image and batch-processing scenarios, making the system versatile while maintaining ease of operation through a unified interface.
Data Source
AI summary
Upon enabling an automatic cropping feature, a computer-implemented method for cropping a plurality of images retrieves a number of images to process among the plurality of images, and selects an image from the plurality of images to determine a set of crop values for the image until the set of crop values for each image have been determined. Determining a set of crop values for an image includes scanning the image from a first side to an opposing side to determine for each scan line of the image an amount representing a degree of color variation among the pixels for that scan line. The amount determined for each scan line is then compared to a threshold value until a crop value for a first area of the image can be determined.