
This paper presents brilliantISP, a modular, open-source HDR image signal processing pipeline for automotive camera applications. Unlike existing open-source ISPs, which employ floating-point arithmetic and are not optimized for HDR automotive use cases, brilliantISP adopts a predominantly fixed-point, unsigned integer architecture with explicit, bounded bit depths at each processing stage, mirroring the constraints of production embedded ISPs while remaining accessible for research and experimentation. The pipeline incorporates a configurable decompanding stage that reconstructs a linear-domain signal from piecewise-companded sensor outputs, supporting sensors with effective dynamic ranges up to 144 dB. Multiple global tone mapping operators are provided, including Reinhard, ACES, and Hable, alongside a Durand-style local tone mapping operator that decomposes the image into base and detail layers for contrast-preserving dynamic range compression. Additional pipeline stages include defect pixel correction, black level correction, lens shading correction, auto white balance, a choice of six demosaicing algorithms, local contrast and edge enhancement, and gamma correction. All stages are configurable via YAML parameter files, and comprehensive debug logging provides block-level execution statistics, dynamic range metrics, bit depth utilization, and histogram outputs to support both algorithm development and ISP tuning studies. The pipeline is validated on imagery from a Sony IMX623 split-pixel HDR fisheye sensor, where decompanded input spans approximately 19.26 EV at 20.7-bit effective depth, compressed to a 3.01 EV, 7.9-bit output after tone mapping and gamma correction. BrilliantISP is intended as a practical research platform for studying HDR tone mapping, demosaicing, and ISP tuning in the context of automotive computational photography.

Currently available visual noise estimation algorithms are primarily developed and calibrated using SDR images, which limits their accuracy in representing the actual noise perceived by humans in HDR content. One key factor often overlooked is the luminance adaptation of the observer, especially when there is a significant contrast between the observed patch and its surrounding area. Moreover, the design of existing test charts, combined with increasingly sophisticated local tone mapping algorithms, introduces new challenges. A prominent issue is the presence of gradients in the final image, which significantly affect algorithmic measurements but have minimal impact on human perception: for instance, a patch may register a Just Noticeable Difference (JND) of 6 compared to a perfectly clean patch with zero noise, despite being visually clean and not visibly different. This paper proposes a new direction for visual noise algorithms and sets the foundations for future research. It presents findings on: 1) A new HDR ruler for visual noise assessment. 2) The impact of various factors (CSF, HPF, gradient correction) on algorithm performance. 3) Evaluation of different color spaces to calculate visual noise metrics.

A controlled experimental setup used multichannel LED lighting to create HDR scenes. Ten observers performed visual matching tasks between real illuminated scenes and HDR display content across eight lighting conditions. Jzazbz, CIECAM16, and CAM16-UCS were evaluated, analysis using STRESS metrics showed CAM16-UCS achieved the best performance for both lightness and colorfulness predictions. Based on these findings, a tone mapping operator was developed utilizing CAM16-UCS color space with local adaptation and gamma adjustments derived from experimental data. The results demonstrate that CAM16-UCS provides superior color appearance prediction for HDR content and serves as an effective foundation for tone mapping applications.

This work provides a novel glass-to-glass metric of local contrast, useful in the context of image quality evaluation of HDR content. This metric, called Local-Contrast Gain (LCG), uses the opto-optical transfer function (OOTF) of the imaging system and its first derivative to compute the incremental ratio between contrast in the scene and contrast on the display. In order to be perceptually meaningful, we chose Weber’s definition of contrast. In order know the OOTF in analytical form and to make the measurement robust to the uncertainty of measurements of the ground truth, we rely on a model that we propose and that expands upon our previously published work. We provide experimental validation of our metric on a variety of target charts, both reflective and transmissive, both in isolation and within complex setups spanning more than six EVs.

This article provides elements to answer the question: how to judge general stylistic color rendering choices made by imaging devices capable of recording HDR formats in an objective manner? The goal of our work is to build a framework to analyze color rendering behaviors in targeted regions of any scene, supporting both HDR and SDR content. To this end, we discuss modeling of camera behavior and visualization methods based on the IC T C P /ITP color spaces, alongside with example of lab as well as real scenes showcasing common issues and ambiguities in HDR rendering.

The motivation for use of biosensors in audiovisual media is made by highlighting problem of signal loss due to wide variability in playback devices. A metadata system that allows creatives to steer signal modifications as a function of audience emotion and cognition as determined by biosensor analysis.

With the release of the Apple iPhone 12 pro in 2020, various features were integrated that make it attractive as a recording device for scene-related computer graphics pipelines. The captured Apple RAW images have a much higher dynamic range than the standard 8-bit images. Since a scene-based workflow naturally has an extended dynamic range (HDR), the Apple RAW recordings can be well integrated. Another feature is the Dolby Vision HDR recordings, which are primarily adapted to the respective display of the source device. However, these recordings can also be used in the CG workflow since at least the basic HLG transfer function is integrated. The iPhone12pro's two Laser scanners can produce complex 3D models and textures for the CG pipeline. On the one hand, there is a scanner on the back that is primarily intended for capturing the surroundings for AR purposes. On the other hand, there is another scanner on the front for facial recognition. In addition, external software can read out the scanning data for integration in 3D applications. To correctly integrate the iPhone12pro Apple RAW data into a scene-related workflow, two command-line-based software solutions can be used, among others: dcraw and rawtoaces. Dcraw offers the possibility to export RAW images directly to ACES2065-1. Unfortunately, the modifiers for the four RAW color channels to address the different white points are unavailable. Experimental test series are performed under controlled studio conditions to retrieve these modifier values. Subsequently, these RAW-derived images are imported into computer graphics pipelines of various CG software applications (SideFx Houdini, The Foundry Nuke, Autodesk Maya) with the help of OpenColorIO (OCIO) and ACES. Finally, it will be determined if they can improve the overall color quality. Dolby Vision content can be captured using the native Camera app on an iPhone 12. It captures HDR video using Dolby Vision Profile 8.4, which contains a cross-compatible HLG Rec.2020 base layer and Dolby Vision dynamic metadata. Only the HLG base layer is passed on when exporting the Dolby Vision iPhone video without the corresponding metadata. It is investigated whether the iPhone12 videos transferred this way can increase the quality of the computer graphics pipeline. The 3D Scanner App software controls the two integrated Laser Scanners. In addition, the software provides a large number of export formats. Therefore, integrating the OBJ-3D data into industry-standard software like Maya and Houdini is unproblematic. Unfortunately, the models and the corresponding UV map are more or less machine-readable. So, manually improving the 3D geometry (filling holes, refining the geometry, setting up new topology) is cumbersome and time-consuming. It is investigated if standard techniques like using the ZRemesher in ZBrush, applying Texture- and UV-Projection in Maya, and VEX-snippets in Houdini can assemble these models and textures for manual editing.

High dynamic range (HDR) technology enables a much wider range of luminances – both relative and absolute – than standard dynamic range (SDR). HDR extends black to lower levels, and white to higher levels, than SDR. HDR enables higher absolute luminance at the display to be used to portray specular highlights and direct light sources, a capability that was not available in SDR. In addition, HDR programming is mastered with wider color gamut, usually DCI P3, wider than the BT.1886 (“BT.709”) gamut of SDR. The capabilities of HDR strain the usual SDR methods of specifying color range. New methods are needed. A proposal has been made to use CIE LAB to quantify HDR gamut. We argue that CIE L* is only appropriate for applications having contrast range not exceeding 100:1, so CIELAB is not appropriate for HDR. In practice, L* cannot accurately represent lightness that significantly exceeds diffuse white – that is, L* cannot reasonably represent specular reflections and direct light sources. In brief: L* is inappropriate for HDR. We suggest using metrics based upon ST 2084/BT.2100 PQ and its associated color encoding, IC<sub>T</sub>C<sub>P</sub>.

Hue linearity is critically important to uniform color spaces and color appearance models. Past studies investigating hue linearity only covered relatively small color gamuts, which was generally acceptable for conventional display technologies. The recent development of HDR and WCG display technologies has motivated the development of new color spaces (e.g., IC<sub>T</sub>C<sub>P</sub> and J<sub>z</sub>a<sub>z</sub>b<sub>z</sub>). The hue linearity of these new color spaces, however, was not verified for the claimed HDR and WCG conditions, due to the lack of constant hue loci data. In this study, an experiment setup was carefully developed to produce HDR and WCG conditions, with the stimulus luminance of 3400 cd/m² and the diffuse white luminance of 1000 cd/m² and the stimulus chromaticities almost covering the Rec. 2020 gamut. The human observers performed a hue matching task, adjusting the hue of the test stimulus, with a hue angle step of 0.2°, at various chroma levels to match that of the reference stimulus at 21 different hues. The derived constant hue loci were used to test the various UCSs and suggested the need to improve the hue linearity of these spaces.

A Triple Conversion Gain (TCG) sensor with all-pixel auto focus based on 2PD of 1.4 um-pitch has been demonstrated for mobile applications. TCG was implemented by sharing adjacent Floating Diffusion (FD) without adding other capacitor. TCG helps to reduce the noise gap or slow the noise increase as user gain increases. An image with a Dynamic Range (DR) of 82.4 dB through a single exposure can be obtained through intra-scene TCG (i-TCG). Through this, a wider range of illuminance environments can be captured in the image. In addition, a more natural image can be obtained by reducing the SNR dip in one image by using TCG.