Tone Mapping via Luminance Sub-Intervals for Mosaic Images
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Solution Overview
Problem
Existing tone mapping methods require complex calculations and are incompatible with portable devices, and they can only handle color images with three components per pixel, failing to efficiently process mosaic images directly from color image sensors.
Innovation Solution
A method that generates a lower dynamic range image by determining pixel luminance, dividing the luminance interval into sub-intervals, and applying a gain based on interpolation, allowing for efficient processing of mosaic color images with reduced calculation power and compatibility with portable devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex tone mapping calculations are used to maintain image quality, then tone mapping accuracy is improved, but calculation power requirements increase making it incompatible with portable devices
Solution Approach 1:
The luminance interval is divided into multiple sub-intervals, allowing the tone mapping to be performed separately in each sub-interval with simplified calculations. This segmentation enables portable devices to process tone mapping in smaller, more manageable steps rather than requiring complex global calculations.
Solution Approach 2:
The method changes the approach by using interpolation based on distance to sub-interval limits rather than complex traditional tone mapping formulas. By parameterizing the gain calculation through simple distance-based interpolation, the system achieves acceptable tone mapping with reduced computational complexity suitable for portable devices.
2Adaptability or versatility
If traditional tone mapping methods are used, then color images with three components per pixel are processed, but mosaic images with single color base values cannot be efficiently processed
Solution Approach 1:
The tone mapping method is designed to work universally with both traditional color images having three components per pixel and mosaic images with single color base values. By using luminance calculation that can handle different image types and applying the same sub-interval interpolation approach to both, the method achieves multi-functionality without sacrificing processing efficiency.
Solution Approach 2:
The method extracts only the essential luminance information from each pixel, regardless of whether the input is a full-color or mosaic image. By focusing on luminance values and their distribution across sub-intervals rather than processing all color components, the method efficiently handles mosaic images while maintaining compatibility with traditional color images.
Data Source
AI summary
Generating by a digital processing device, of a first digital image from a second digital image, by: for each pixel of the second image, determining a pixel luminance; dividing the interval ranging from the lowest to the highest luminance into a plurality of sub-intervals; and determining the value of at least one pixel of the first image by multiplying the value of a pixel of the second image by a gain determined by interpolation by taking into account the distance of the pixel luminance of the second image to the limits of the sub-interval containing this luminance.