Adaptive Local Reshaping for SDR to HDR Conversion
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Solution Overview
Problem
Existing image processing technologies struggle to effectively convert images of standard dynamic range (SDR) to high dynamic range (HDR) while maintaining accurate local contrast and color saturation, often resulting in artifacts such as halos due to uniform reshaping across all pixels.
Innovation Solution
Implement local reshaping techniques that apply different reshaping functions to individual pixels based on local brightness levels, using multi-level edge-preserving filtering and predictive models to enhance local contrast and color saturation in HDR images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform reshaping is applied across all pixels, then the conversion from SDR to HDR is simplified, but local contrast accuracy deteriorates and artifacts such as halos appear
Solution Approach 1:
The patent applies different reshaping functions to different local regions of the image based on local brightness characteristics. Instead of using a single uniform reshaping function for all pixels, the system divides the image into multiple local regions and selects appropriate reshaping functions for each region, thereby maintaining local contrast accuracy while avoiding artifacts like halos.
2Manufacturing precision
If local reshaping is applied to enhance local contrast, then local contrast and color saturation improve, but processing complexity increases
Solution Approach 1:
The patent segments the image processing task by dividing it into multiple processing stages: brightness level determination, local region identification, selective reshaping function application, and artifact suppression. This segmentation allows the system to apply complex local reshaping operations only where necessary, rather than uniformly across the entire image, thereby managing processing complexity more effectively.
Solution Approach 2:
The patent applies partial action by selectively applying reshaping operations only to specific local regions that benefit from contrast enhancement, rather than processing the entire image uniformly. This approach reduces unnecessary processing complexity while maintaining local contrast accuracy in critical areas.
3Manufacturing precision
If aggressive reshaping is used to improve HDR quality, then local contrast enhances, but artifacts such as halos increase
Solution Approach 1:
The patent implements preliminary anti-action by incorporating artifact suppression mechanisms that counteract halo effects before they become prominent. The system monitors local brightness transitions and applies corrective measures in advance to prevent excessive contrast enhancement from generating visible artifacts, thereby maintaining HDR quality without introducing harmful halos.
Data Source
Figure 1~2A
Figure 2B
Figure 2C
AI summary
A global index value is generated for selecting a global reshaping function for an input image of a relatively low dynamic range using luma codewords in the input image. Image filtering is applied to the input image to generate a filtered image. The filtered values of the filtered image provide a measure of local brightness levels in the input image. Local index values are generated for selecting specific local reshaping functions for the input image using the global index value and the filtered values of the filtered image. A reshaped image of a relatively high dynamic range is generated by reshaping the input image with the specific local reshaping functions selected using the local index values.