Chroma Reshaping for HDR Images Using Binned Scale Factors
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Solution Overview
Problem
Current chroma reshaping techniques for high dynamic range (HDR) images often result in oversaturated and desaturated pictures of poor visual quality, especially when trying to maintain backward compatibility with standard dynamic range (SDR) decoders, as they fail to accurately reconstruct original HDR signals.
Innovation Solution
The proposed solution involves a content-dependent maximal scale factor approach for chroma reshaping, where the range of luma pixel values is divided into bins, and maximal scale factors are generated based on chroma pixel values within each bin, allowing for a forward reshaping function that maps reshaped luma values to chroma scale factors, thereby preventing clipping and ensuring reversibility of the reshaping process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional chroma reshaping techniques are applied to maintain backward compatibility with SDR decoders, then compatibility is improved, but visual quality deteriorates due to oversaturation and desaturation
Solution Approach 1:
The patent segments the luma pixel value range into multiple bins and applies different chroma scale factors to each bin. This content-dependent segmentation allows the system to maintain compatibility with SDR decoders while preserving HDR visual quality by adapting chroma reshaping parameters locally rather than applying a global transformation.
Solution Approach 2:
The patent implements dynamic chroma scale factors that vary based on the content within each luma bin. The scale factors are generated adaptively based on chroma pixel values, allowing the system to dynamically adjust chroma reshaping parameters to prevent both oversaturation and desaturation while maintaining backward compatibility.
2Reliability
If chroma reshaping is applied to prevent clipping, then signal reconstruction is improved, but visual quality deteriorates due to oversaturation
Solution Approach 1:
The patent changes the chroma scale factor parameter dynamically based on the luma bin and chroma pixel values within each bin. By adjusting this parameter content-dependently, the system prevents clipping and ensures reversible signal reconstruction while avoiding oversaturation that would degrade visual quality.
3Device complexity
If fixed chroma scale factors are used for reshaping, then processing complexity is reduced, but visual quality deteriorates due to inability to handle diverse content
Solution Approach 1:
The patent divides the luma range into bins and applies different chroma scale factors to each bin, segmenting the processing task. This allows the system to handle diverse content effectively by applying appropriate scale factors to different content regions without requiring a single complex global transformation.
Solution Approach 2:
The patent performs preliminary generation of chroma scale factors based on chroma pixel values within each luma bin before applying the final reshaping. This preliminary action enables the system to prepare appropriate transformation parameters in advance, reducing real-time processing complexity while maintaining high visual quality.
Data Source
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AI summary
Methods and systems for adaptive chroma reshaping are discussed. Given an input image, a luma-reshaped image is first generated based on its luma component. For each chroma component of the input image, the range of the pixel values in the luma reshaped image is divided into bins, and for each bin a maximal scale factor is generated based on the chroma pixel values in the input image corresponding to the pixels of the luma reshaped image in the bin. A forward reshaping function is generated based on a reference reshaped function and the maximal scale factors, and reshaped chroma pixel values for the chroma component are generated based on the forward reshaping function and the corresponding pixel values in the luma reshaped image. Implementations options using look-up tables for mobile platforms with limited computational resources are also described.