
Characterization of a high dynamic range (HDR) display’s performance can be largely defined by its contrast and peak luminance. Prior work has studied this question for virtual reality (VR) using a haploscopic HDR setup, but it is not obvious if those results are transferrable to a more traditional viewing setting, such as direct view. In this work, we conducted a study to measure user preference for different contrast and peak luminance parameters in this scenario, and develop a perceptual just-objectionable-difference (JOD) scale to quantify preference scores. This is accomplished by studying contrast and peak luminance conditions across several orders of magnitude, shown on a professional HDR display with peak luminance of 1,000 nits and 1,000,000:1 contrast. The data is used to develop a computational model that can drive display design and future standardization of the definition of HDR, in terms of human preference.

Reference-white placement is a central issue in HDR rendering because it determines the viewer’s adaptation state and strongly influences perceived color appearance. In HDR scenes that contain both reflective surfaces and bright emissive elements, it remains unclear how diffuse white should be placed on the display and how highlights should be tone mapped to preserve appearance. In this work, we investigate how reference-white placement and highlight tone mapping affect the perceptual reproduction of emissive colors in HDR scenes. To address this question, we captured a controlled HDR scene containing both reflective and emissive color targets and rendered it on a professional HDR display using different rendering strategies. These included varying the displayed diffuse-white luminance and comparing global tone mapping with selective processing of emissive regions. Due to scene complexities and the absence of standard color-difference metrics designed for such applications, our work relies on perceptual assessments. Observers viewed the real scene and then evaluated the display renderings in terms of appearance-based criteria. The results show that reference-white placement and tone-mapping strategy jointly influence perceived image quality and color appearance. In particular, the initial findings suggest that separately processing reflective and emissive regions—preserving colorimetric reproduction for reflective areas while compressing emissive highlights—produces the most favorable overall renderings.

A low-noise CMOS image sensor (CIS) with intra-scene high-dynamic-range (HDR) feature is presented. The design employs in-pixel 5-transistor (5T) operational transconductance amplifier (OTA) in open-loop configuration to achieve high gain to amplify a weak signal under low illumination. A unity-gain configuration is adopted for high level illumination, and it can avoid saturation at the output, thereby enhancing the overall dynamic range. Noise suppression is further enhanced through analog correlated double sampling (CDS) in the column programmable gain amplifier (PGA) and digital CDS after analog-to-digital conversion, effectively suppressing kT/C noise, low-frequency noise and offset. A prototype was developed using 180 nm CIS process, and the characterization results show that the sensor has a low input-referred noise of 0.67 e−r ms and dynamic range of 80 dB. The proposed approach provides a practical path toward low-noise, widedynamic-range CIS architectures and can be extended to future pixel-parallel readout schemes.

Conventional image quality metrics (IQMs), such as PSNR and SSIM, are designed for perceptually uniform gamma-encoded pixel values and cannot be directly applied to perceptually non-uniform linear high-dynamic-range (HDR) colors. Similarly, most of the available datasets consist of standard-dynamic-range (SDR) images collected in standard and possibly uncontrolled viewing conditions. Popular pre-trained neural networks are likewise intended for SDR inputs, restricting their direct application to HDR content. On the other hand, training HDR models from scratch is challenging due to limited available HDR data. In this work, we explore more effective approaches for training deep learning-based models for image quality assessment (IQA) on HDR data. We leverage networks pre-trained on SDR data (source domain) and re-target these models to HDR (target domain) with additional fine-tuning and domain adaptation. We validate our methods on the available HDR IQA datasets, demonstrating that models trained with with our combined recipe outperform previous baselines, converge much quicker, and reliably generalize to HDR inputs.

A significant challenge in tone mapping is to preserve the perceptual quality of high dynamic range (HDR) images when mapping them to standard dynamic range (SDR) displays. Most of the tone mapping operators (TMOs) compress the dynamic range without considering the surround viewing conditions such as average, dim and dark, leading to the unsatisfactory perceptual quality of the tone mapped images. To address this issue, this work focuses on utilizing CIECAM16 brightness, colorfulness, and hue perceptual correlates. The proposed model compresses the perceptual brightness and transforms the colors from HDR images using CIECAM16 color adaptations under display conditions. The brightness compression parameter was modeled via a psychophysical experiment. The proposed model was evaluated using two psychophysical experimental datasets (Rochester Institute of Technology (RIT) and Zhejiang University (ZJU) datasets).

A lightweight learning-based exposure bracketing strategy is proposed in this paper for high dynamic range (HDR) imaging without access to camera RAW. Some low-cost, power-efficient cameras, such as webcams, video surveillance cameras, sport cameras, mid-tier cellphone cameras, and navigation cameras on robots, can only provide access to 8-bit low dynamic range (LDR) images. Exposure fusion is a classical approach to capture HDR scenes by fusing images taken with different exposures into a 8-bit tone-mapped HDR image. A key question is what the optimal set of exposure settings are to cover the scene dynamic range and achieve a desirable tone. The proposed lightweight neural network predicts these exposure settings for a 3-shot exposure bracketing, given the input irradiance information from 1) the histograms of an auto-exposure LDR preview image, and 2) the maximum and minimum levels of the scene irradiance. Without the processing of the preview image streams, and the circuitous route of first estimating the scene HDR irradiance and then tone-mapping to 8-bit images, the proposed method gives a more practical HDR enhancement for real-time and on-device applications. Experiments on a number of challenging images reveal the advantages of our method in comparison with other state-of-the-art methods qualitatively and quantitatively.

Content created in High Dynamic Range (HDR) and Wide Color Gamut (WCG) is becoming more ubiquitous, driving the need for reliable tools for evaluating the quality across the imaging ecosystem. One of the simplest techniques to measure the quality of any video system is to measure the color errors. The traditional color difference metrics such as ΔE00 and the newer HDR specific metrics such as ΔEZ and ΔEITP compute color difference on a pixel-by-pixel basis which do not account for the spatial effects (optical) and active processing (neural) done by the human visual system. In this work, we improve upon the per-pixel ΔEITP color difference metric by performing a spatial extension similar to what was done during the design of S-CIELAB. We quantified the performance using four standard evaluation procedures on four publicly available HDR and WCG image databases and found that the proposed metric results in a marked improvement with subjective scores over existing per-pixel color difference metrics.

There are an increasing number of databases describing subjective quality responses for HDR (high dynamic range) imagery with various distortions. The dominant distortions across the databases are those that arise from video compression, which are primarily perceived as achromatic, but there are some chromatic distortions due to 422 and other chromatic sub-sampling. Tone mapping from the source HDR levels to various levels of reduced capability SDR (standard dynamic range) are also included in these databases. While most of these distortions are achromatic, tone-mapping can cause changes in saturation and hue angle when saturated colors are in the upper hull of the of the color space. In addition, there is one database that specifically looked at color distortions in an HDR-WCG (wide color gamut) space. From these databases we can test the improvements to well-known quality metrics if they are applied in the newly developed color perceptual spaces (i.e., representations) specifically designed for HDR and WCG. We present results from testing these subjective quality databases to computed quality using the new color spaces of Jzazbz and ICTCP, as well as the commonly used SDR color space of CIELAB.

Proposed for the first time is a novel calibration empowered minimalistic multi-exposure image processing technique using measured sensor pixel voltage output and exposure time factor limits for robust camera linear dynamic range extension. The technique exploits the best linear response region of an overall nonlinear response image sensor to robustly recover via minimal count multi-exposure image fusion, the true and precise scaled High Dynamic Range (HDR) irradiance map. CMOS sensor-based experiments using a measured Low Dynamic Range (LDR) 44 dB linear region for the technique with a minimum of 2 multi-exposure images provides robust recovery of 78 dB HDR low contrast highly calibrated test targets.

Experimentally demonstrated for the first time is Coded Access Optical Sensor (CAOS) camera empowered robust and true white light High Dynamic Range (HDR) scene low contrast target image recovery over the full linear dynamic range. The 90 dB linear HDR scene uses a 16 element custom designed test target with low contrast 6 dB step scaled irradiances. Such camera performance is highly sought after in catastrophic failure avoidance mission critical HDR scenarios with embedded low contrast targets.