ROI-Biased Tone Mapping for Dynamic Range Compression
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Solution Overview
Problem
Tone mapping in image processing often faces challenges in balancing the compression of dynamic range to fit output devices without causing grayscale reversal or false gradients, which requires computationally costly spatial processing or complex global-local optimization.
Innovation Solution
A method is introduced to generate an image's tone curve based on a weighted combination of the entire image's statistics and a user-specified region-of-interest (ROI), allowing for adaptive tone mapping that prioritizes the ROI's significance, thereby reducing grayscale reversal and enhancing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If global tone mapping is used to compress dynamic range, then the output fits device dynamic range, but local contrast is lost and grayscale reversal occurs
Solution Approach 1:
The patent applies local quality by computing separate tone mapping curves for different spatial regions of the image. Each region's tone curve is derived from its local luminance histogram, allowing different parts of the image to have different tone mapping characteristics. This enables preservation of local contrast in each region while still achieving overall dynamic range compression across the entire image.
2Manufacturing precision
If local tone mapping is used to preserve local contrast, then spatial details are maintained, but computational cost increases significantly
Solution Approach 1:
The patent segments the image into multiple regions and computes tone mapping curves for each segment independently. By dividing the image into manageable regions, the computational complexity is reduced compared to processing the entire image with complex global-local optimization, while still maintaining local contrast within each segmented region.
3Manufacturing precision
If complex global-local optimization is used to minimize grayscale reversal, then tone mapping accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by computing local luminance histograms and deriving tone curves for each region before applying the final tone mapping. This pre-computation of region-specific statistics and curves eliminates the need for iterative global-local optimization during the actual tone mapping process, significantly reducing processing time while maintaining accuracy in minimizing grayscale reversal.
Data Source
AI summary
Methods, devices and computer readable instructions to generate region-of-interest (ROI) tone curves are disclosed. One method includes obtaining a statistic for an entire image such as, for example, a luminance statistic. The same statistic may then be found for a specified ROI of the image. A weighted combination of the statistic of the entire image and the statistic of the ROI yields a combined statistic which may then be converted to a ROI-biased tone curve. The weight used to combine the two statistics may be selected to emphasize or de-emphasize the role of the ROI's statistic in the final tone curve.