Blind Local Reshaping for HDR Image Artifact Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing HDR image coding techniques apply a single global reshaping function to all pixels, leading to coding artifacts and suboptimal image quality, and require metadata transmission for reconstruction, limiting efficiency and compatibility.
Innovation Solution
Employ a family of local reshaping functions selected based on spatial information, with a decoder iteratively estimating the mapping indices to reconstruct the HDR image without additional metadata, using a blind local reshaping method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single global reshaping function is applied to all pixels, then device complexity is reduced, but image quality deteriorates due to coding artifacts
Solution Approach 1:
The patent divides the image into multiple local regions and applies different reshaping functions to each region based on local characteristics such as luminance histograms. This segmentation approach allows the system to achieve high image quality through localized optimization while keeping the overall system manageable by processing regions independently rather than requiring a single complex global function.
Solution Approach 2:
The patent implements local reshaping where each region undergoes reshaping optimized for its specific characteristics. By analyzing local luminance histograms and applying region-specific reshaping functions, the system achieves superior local image quality and reduces coding artifacts compared to uniform global reshaping, while maintaining reasonable system complexity through modular regional processing.
2Productivity
If local reshaping functions are used to improve image quality, then coding efficiency improves, but device complexity increases due to multiple reshaping functions
Solution Approach 1:
The patent segments the image into multiple regions and applies different reshaping functions to each segment based on local luminance characteristics. This approach improves coding efficiency by optimizing compression for each region's specific distribution while managing complexity through independent regional processing rather than requiring a single overly complex global function.
Solution Approach 2:
The patent changes reshaping parameters locally by analyzing luminance histograms in different regions and adapting reshaping function parameters accordingly. This allows the system to achieve high coding efficiency by matching reshaping characteristics to local content properties while avoiding the need for a single complex function with many parameters, thus managing device complexity effectively.
3Manufacturing precision
If metadata is transmitted for HDR reconstruction, then image quality is improved, but loss of information increases due to additional data requirements
Solution Approach 1:
The patent extracts only the essential reshaping function parameters needed for HDR reconstruction rather than transmitting complete metadata about the original HDR image. By taking out only the critical reshaping information and discarding redundant data, the system achieves good reconstruction quality while minimizing information loss and transmission overhead.
Solution Approach 2:
The patent creates simplified copies of the reshaping functions as parameter sets that can be transmitted efficiently. Instead of transmitting full HDR metadata, the system transmits compact parameter representations of the reshaping functions that were applied, allowing the decoder to reconstruct HDR images with acceptable quality while significantly reducing data transmission requirements.
4Adaptability or versatility
If blind local reshaping is implemented without metadata, then compatibility with legacy decoders is improved, but measurement precision decreases due to iterative estimation
Solution Approach 1:
The patent implements blind local reshaping that produces output compatible with legacy decoders by avoiding the transmission of format-specific metadata. The reshaped image can be decoded by any standard decoder, providing universal compatibility. The system achieves this by embedding all necessary information in the image data itself rather than requiring additional metadata streams that would reduce compatibility.
Solution Approach 2:
The patent employs iterative estimation with feedback mechanisms where the decoder refines its estimation of mapping indices through multiple passes. The feedback from intermediate reconstruction results guides the optimization of mapping indices, allowing the system to achieve acceptable precision despite the absence of metadata, while maintaining compatibility with legacy decoders that don't support the advanced blind reshaping feature.
Data Source
AI summary
In an encoder, a high-dynamic range (HDR) image is encoded using a family of local forward reshaping functions selected according to an array of forward mapping indices (FMI) indicating which local forward reshaping function needs to be used for each pixel in the HDR image to generate a reshaped standard dynamic range (SDR) image. A decoder, given the reshaped SDR image, iteratively generates a reconstructed HDR image and estimated reshaped SDR images by adjusting a local FMI array and a local array of backward mapping indices (BMI) until an error metric related to the difference between the local BMI and FMI arrays and the difference between the estimate SDR images and the reshaped SDR image satisfy a convergence criterion. Techniques for generating families of local forward reshaping functions and local backward reshaping functions based on a global forward reshaping function are also presented.