Image Bit-Depth Reduction With Histogram Peak Detail Recovery
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Solution Overview
Problem
Existing methods for reducing the dynamic range of images, particularly in scenarios with limited computational resources, fail to preserve image details effectively, especially when transitioning from high to low dynamic range.
Innovation Solution
A method involving histogram analysis to identify peak regions, calculating pixel value differences, and applying these differences to a transformed image after reducing its bit depth, using computationally efficient transformations like linear stretching to enhance detail preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If linear transformation is applied to reduce bit depth, then computational complexity is reduced, but image details are lost
Solution Approach 1:
The patent applies preliminary action by calculating pixel value differences from the first image before the bit depth reduction transformation is applied to the second image. These pre-calculated differences are then added to the transformed image to compensate for lost details, allowing the use of simple linear transformation while preserving image information.
2Loss of information
If sophisticated non-linear transformations are applied to preserve details, then image details are preserved, but computational complexity increases
Solution Approach 1:
The patent introduces an intermediary approach by using a reference first image to calculate pixel value differences that serve as a mediator between the original high-bit-depth image and the transformed low-bit-depth image. This intermediary data compensates for the simplicity of linear transformation, preserving details without requiring complex non-linear transformations.
3Adaptability or versatility
If bit depth is reduced from first bit depth to second bit depth, then dynamic range is reduced for compatibility, but image quality deteriorates
Solution Approach 1:
The patent applies local quality by adding pixel value differences specifically to pixels that underwent bit depth reduction. This localized compensation ensures that quality is maintained in regions where it matters most, while allowing the overall bit depth to be reduced for compatibility with display devices and processing pipelines.
Data Source
AI summary
A method for reducing a dynamic range of an image, whereby first and second images have pixel values of a first bit depth. One or more peak regions and a representative pixel value for each peak region is identified in a histogram of the pixel values in the first image. For pixel values in the first image that are within a peak region, a pixel value difference is calculated as a difference between the pixel value and the representative pixel value of the peak region. For pixel values in the first image that are outside of the peak regions, the pixel value difference is zero. The pixel values of the second image are transformed to reduce the bit depth to a second bit depth. The pixel value differences calculated from the first image are then added to the pixel values of the transformed second image.