Metadata-Based Image Processing for Color Gamut Correction
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Solution Overview
Problem
Display apparatuses with different color representation capabilities face challenges in accurately reproducing images with color gamuts that do not match their own, leading to suboptimal color reproduction and dynamic range issues.
Innovation Solution
A method and apparatus that split an image into regions based on spatial proximity and color similarity, acquire and compare color mapping functions between these regions and a reference image, and generate metadata for gamut correction, tone mapping, and saturation correction to align the image with the display's capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If global color mapping is applied to the entire image, then color gamut correction is simplified, but region-specific color accuracy deteriorates
Solution Approach 1:
The image is divided into multiple regions based on spatial proximity and color similarity of pixels. Each region is processed independently with its own color mapping function, allowing region-specific color accuracy while maintaining overall system manageability.
Solution Approach 2:
Different color mapping functions are applied to different regions of the image based on their specific color characteristics. This ensures that each region receives optimized color correction tailored to its local properties, improving overall color reproduction accuracy.
2Manufacturing precision
If region-wise color mapping is applied to improve color accuracy, then color reproduction accuracy is improved, but processing complexity increases
Solution Approach 1:
Regions with similar color mapping characteristics are merged into similar color mapping regions. This reduces the total number of distinct processing zones while maintaining color accuracy, thereby reducing processing complexity.
Solution Approach 2:
The color mapping function acquisition process is designed to handle multiple regions simultaneously by comparing color information across corresponding regions. This multi-functional approach reduces overall processing steps compared to handling each region independently.
3Manufacturing precision
If color mapping functions are acquired for all regions independently, then color accuracy is maximized, but processing time increases
Solution Approach 1:
The image is pre-divided into regions based on spatial proximity and color similarity before color mapping function acquisition. This preliminary organization allows for more efficient processing by grouping regions that can be processed together, reducing overall processing time.
Solution Approach 2:
Color mapping functions from representative regions are copied to similar regions, reducing the need to compute separate functions for every region. This maintains color accuracy while significantly reducing processing time through function reuse.
Data Source
AI summary
Disclosed are a method and apparatus for generating local metadata including position information of a similar color mapping region and a color mapping function of the similar color mapping region and a method and apparatus for correcting color components 5 of a pixel in a similar color mapping region based on local metadata.