Automated Color Correction Grouping Multiple Images
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Solution Overview
Problem
Existing automated color correction methods for images are limited in their ability to accurately balance colors across multiple images, often producing undesirable changes due to reliance on single-image analysis without considering capture conditions or intended color settings.
Innovation Solution
A method that determines characteristics of multiple source images, groups them based on similarities, and applies tailored color corrections to each group, using color data, timestamps, and identified objects to ensure accurate and consistent color balancing across similar images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated color correction is applied to individual images using histogram analysis, then color balancing is improved, but accuracy deteriorates due to inability to consider capture conditions and intended color settings
Solution Approach 1:
The patent segments the image processing task by separating images into groups based on shared characteristics (capture conditions, settings, subjects). Instead of treating each image independently, the system divides them into meaningful categories where color corrections can be applied more accurately within each group while maintaining consistency across similar images.
Solution Approach 2:
The patent adds new dimensions to the color correction process by incorporating metadata characteristics (capture conditions, camera settings, timestamps, subjects) beyond just the visual histogram data. This multi-dimensional approach allows the system to understand the context of each image and apply more accurate corrections.
2Measurement precision
If manual color examination and adjustment is performed for each image, then color accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary grouping of images based on their characteristics before applying color corrections. By pre-organizing images into groups with similar properties, the system prepares the data structure needed for efficient batch processing, enabling automated corrections that maintain accuracy without manual intervention for each image.
Solution Approach 2:
The patent applies color corrections derived from representative images or group characteristics to multiple similar images. Instead of manually adjusting each image individually, the system creates correction profiles based on group analysis and applies these corrections across multiple images, significantly reducing time while maintaining consistency.
3Productivity
If existing automated color correction functions are applied to multiple images, then processing speed is improved, but color consistency deteriorates due to inability to group images by characteristics
Solution Approach 1:
The patent segments the batch of images into distinct groups based on shared characteristics such as capture conditions, camera settings, timestamps, and subjects. This segmentation ensures that images with similar properties are processed together, maintaining color consistency within each group while allowing different correction strategies for different groups.
Solution Approach 2:
The system changes the parameters used for color correction by incorporating multiple characteristics (beyond just histogram data) such as capture conditions, camera settings, and subject information. These parameter changes enable the system to maintain color consistency across similar images by using comprehensive grouping criteria.
Data Source
AI summary
In some implementations, a method provides color corrections based on multiple images. In some implementations, a method includes determining one or more characteristics of each of a plurality of source images and determining one or more similarities between the one or more characteristics of different source images. The source images are grouped into one or more groups of one or more target images based on the determined similarities. The method determines and applies one or more color corrections to the one or more target images in at least one of the groups.