GSDF-Based Image Data Mapping for HDR-to-SDR Display Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image display technologies struggle to accurately render high dynamic range (HDR) images on standard dynamic range (SDR) displays due to perceptual nonlinearity in human vision, leading to visually noticeable errors and artifacts like banding and contour distortion.
Innovation Solution
A contrast sensitivity function (CSF) model is used to determine just noticeable differences (JNDs) across various light levels and spatial frequencies, generating a reference gray scale display function (GSDF) that optimally maps digital code values to gray levels, preserving perceptual details and minimizing artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If HDR images are displayed on SDR displays using conventional linear mapping methods, then the dynamic range is extended, but perceptual errors and artifacts like banding and contour distortion become visually noticeable
Solution Approach 1:
The patent applies parameter changes by transforming the linear luminance mapping into a perceptual nonlinearity-based mapping. Specifically, it uses a contrast sensitivity function (CSF) model to calculate just-noticeable differences (JNDs) and adjusts the mapping parameters according to human visual perception characteristics at different light levels and spatial frequencies, thereby reducing perceptual errors while maintaining extended dynamic range
Solution Approach 2:
The patent implements local quality by applying different mapping characteristics to different regions of the luminance range. Instead of using a uniform linear mapping, it calculates JNDs at specific light levels and spatial frequencies, creating a non-uniform mapping that adapts to local perceptual sensitivity variations. This ensures optimal visual quality across the entire dynamic range while minimizing artifacts in specific regions
2Adaptability or versatility
If device-specific image transformations are applied during image delivery, then images are adapted to display capabilities, but large amounts of visually noticeable errors occur in rendered images
Solution Approach 1:
The patent introduces an intermediary mapping function based on the contrast sensitivity function (CSF) model that serves as a mediator between the source image data and the display device capabilities. This intermediary uses JND calculations to create a perceptually uniform mapping that preserves image quality while adapting to different display capabilities, thereby reducing rendering errors
Solution Approach 2:
The patent applies preliminary action by pre-calculating the contrast sensitivity function (CSF) model parameters and JND values before image rendering. The CSF model is configured in advance with appropriate light levels and spatial frequencies, and the mapping table is pre-computed, allowing for efficient and accurate real-time rendering without introducing perceptual errors
3Device complexity
If equal luminance quantization steps are used, then the data representation is simple, but perceptual details are lost due to uniform distribution not matching human vision characteristics
Solution Approach 1:
The patent changes the parameter distribution from uniform to non-uniform by applying the contrast sensitivity function (CSF) model. Instead of equal luminance quantization steps, it calculates JND-based step sizes that vary according to light level and spatial frequency, creating a perceptually uniform distribution that preserves critical visual details while maintaining manageable data representation complexity
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
A handheld imaging device has a data receiver that is configured to receive reference encoded image data. The data includes reference code values, which are encoded by an external coding system. The reference code values represent reference gray levels, which are being selected using a reference grayscale display function that is based on perceptual non-linearity of human vision adapted at different light levels to spatial frequencies. The imaging device also has a data converter that is configured to access a code mapping between the reference code values and device-specific code values of the imaging device. The device-specific code values are configured to produce gray levels that are specific to the imaging device. Based on the code mapping, the data converter is configured to transcode the reference encoded image data into device-specific image data, which is encoded with the device-specific code values.