Local Tone Curve Computation for High Dynamic Range Image Rendering
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Solution Overview
Problem
Current digital image processing techniques, such as tone mapping, often introduce artifacts like halos and unnatural contrasts when adjusting the dynamic range of images for display on media with limited capabilities, failing to effectively render high dynamic range images on lower bit depth displays.
Innovation Solution
The method employs local tone curve computation to adjust the luminance of images by dividing them into blocks, generating tone adjustment values that minimize artifacts, and applying these values to each block's histogram to match target luminance values, thereby optimizing the dynamic range for aesthetically pleasing results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If tone mapping is used to adjust the dynamic range of digital images, then the dynamic range is reduced for display on limited capability media, but artifacts such as halos, rings, overly saturated colors, and unnatural appearance are introduced
Solution Approach 1:
The image is divided into multiple blocks, and a separate tone curve is computed for each block based on its local histogram. This segmentation allows different regions of the image to have different tone adjustments, preventing the introduction of artifacts like halos and rings that occur with global tone mapping approaches.
Solution Approach 2:
The patent applies local quality by computing unique tone curves for each image block rather than using a single global tone curve. Each block's tone curve is optimized for its specific luminance distribution, preserving local contrast and details while avoiding the unnatural appearance and color saturation issues caused by uniform global adjustments.
2Adaptability or versatility
If the dynamic range of an image is compressed for display on limited capability media, then the image can be displayed on monitors and printers, but details in dark and bright areas become invisible
Solution Approach 1:
By dividing the image into blocks and computing local tone curves for each, the patent preserves details in both dark and bright areas that would otherwise be lost in global compression. Each block's local histogram ensures that detail preservation is adapted to the specific luminance characteristics of that region.
Solution Approach 2:
The patent changes the luminance parameters locally within each image block by applying block-specific tone curves. This allows the display capabilities of limited media to be matched while preserving image details through localized parameter adjustments rather than uniform global changes.
3Manufacturing precision
If local tone curve computation is applied to each image block, then local contrast and image details are preserved, but the computational complexity increases
Solution Approach 1:
The patent segments the image into blocks to enable local tone curve computation, which improves tone adjustment precision for each region. While this increases computational complexity compared to global tone mapping, the segmentation into manageable blocks makes the computation feasible and allows for optimized processing.
Data Source
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AI summary
Image tone adjustment using local tone curve computation may be utilized to adjust luminance ranges for images. Image tone adjustment using local tone curve computation may reduce the overall contrast of an image, while maintaining local contrast in smaller areas, such as in images capturing brightly lit scenes where the difference in intensity between brightest and darkest areas is large. A desired brightness representation of the image may be generated including target luminance values for corresponding blocks of the image. For each block, one or more tone adjustment values may be computed, that when jointly applied to the respective histograms for the block and neighboring blocks results in the luminance values that match corresponding target values. The tone adjustment values may be determined by solving an under-constrained optimization problem such that optimization constraints are minimized. The image may then be adjusted according to the computed tone adjustment values.