Image Color Tone Alignment via Regional Luma Segmentation
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Solution Overview
Problem
Existing filter migration methods for images and videos fail to achieve a natural and consistent color tone alignment between original and reference images/videos, as they rely on matching histograms of color channels, resulting in suboptimal effects.
Innovation Solution
The method involves dividing original and reference images into regions based on luma values and calculating transformation matrices for each region to align color tones, using covariance matrices to determine these matrices, and then color mapping the original image according to these matrices to produce a target image with improved naturalness and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If histogram matching of color channels is used for filter migration, then the color tone alignment between original and reference images is achieved, but the naturalness and visual coherence of the result deteriorates
Solution Approach 1:
The image is divided into multiple regions based on luma value ranges. Different transformation matrices are calculated and applied to different regions, allowing for more nuanced color tone adjustment that preserves natural appearance while achieving alignment with the reference image.
Solution Approach 2:
Different regions of the image receive different transformation matrices tailored to their specific luma characteristics. This localised approach ensures that each region's color tone is adjusted appropriately, maintaining naturalness while achieving overall color tone alignment with the reference image.
2Device complexity
If a single transformation matrix is used for the entire image, then the processing complexity is reduced, but the color tone consistency across different luminance regions deteriorates
Solution Approach 1:
The image is segmented into multiple regions based on luma values, with each region receiving a dedicated transformation matrix. This segmentation enables precise color tone consistency across different luminance regions while keeping the processing approach systematic and manageable.
Solution Approach 2:
The transformation matrix parameters are changed according to the luma value ranges of different regions. By adapting the transformation parameters to local characteristics, the method achieves superior color tone consistency across varying luminance conditions compared to a single global matrix.
3Productivity
If histogram matching is applied to each color channel separately, then the computational efficiency is improved, but the overall filter migration effect deteriorates
Solution Approach 1:
The image processing is segmented into region-based operations where transformation matrices are calculated and applied to specific luma ranges. This approach maintains computational efficiency by avoiding full histogram matching while achieving better filter migration effects through targeted regional adjustments.
Solution Approach 2:
Instead of applying uniform histogram matching to all pixels, the method applies localised transformation matrices to regions with similar luma characteristics. This local quality approach produces superior filter migration effects by accounting for the different color relationships in different luminance regions.
Data Source
AI summary
The present disclosure provides an image processing method and device. Wherein, an original image is divided into a plurality of original regions, the plurality of original regions at least comprising a first region and a second region; wherein the luma values of pixel points in the first region are greater than the luma values of pixel points in the second region, a reference image is divided into into a plurality of reference regions, the reference regions at least comprising a third region and a fourth region; wherein the luma values of pixel points in the third region are greater than the luma values of pixel points in the fourth region, a transformation matrix for the first region is obtained based on color values of pixel points in the first region and color values of pixel points in the third region, and a transformation matrix for the second region is obtained based on color values of pixel points in the second region and color values of pixel points in the fourth region, and color mapping is performed on the original image according to the transformation matrix for the first region and the transformation matrix for the second region to obtain a target image. In this way, the target image is more natural, and is closer to the reference image in color tone, and the filter migration effect is good.