Excel to HTML
TUESDAY 3 MARCH 2026
Color Perception and Display
Session Chair: Mylene Farias
09:30 - 10:30
Grand Peninsula A
09:30IQSP-244
Color perception through tinted eyewear: Theoretical and computational perspective, Sanaz Aghamohammadi Kalkhoran, Rochester Institute of Technology (US); Shuyi Zhao, Rochester Institute of Technology (US); Likhitha Nagahanumaiah, Rochester Institute of Technology; Christopher Thorstenson, Rochester Institute of Technology (US); Susan Farnand, Rochester Institute of Technology (US) [view abstract]
This study presents a modeling framework for predicting color perception through tinted eyewear with known spectral transmittance. The proposed model integrates multiple parameters, including luminance level, illuminant type, and color pair datasets that span both large and small color differences. Several established metrics (such as CIEDE2000, HyAB, Cone contrast, ?LMS, and related measures) is tested as predictors of visual performance. The framework enables systematic comparison of parameter combinations to identify the most reliable predictor of perceptual outcomes. The aim is to provide a practical and scientifically grounded tool for assessing the performance of tinted eyewear in applied settings, ranging from vision research to product evaluation.
09:50IQSP-245
Color perception through tinted eyewear: An experimental evaluation using Visual Search with reflective samples, Shuyi Zhao, (China (Mainland)); Sanaz Kalkhoran, ; Likhitha Nagahanumaiah, ; Susan Farnand, ; Christopher Thorstenson, [view abstract]
Tinted eyewear alters the spectral information reaching the human eye, potentially influencing visual performance in real-world tasks. Our previous work quantified changes in color discrimination ability under tinted eyewear using a psychophysical experiment. The present study extends this investigation by employing a visual search method to evaluate perceptual sensitivity. Two psychophysical experiments were conducted to evaluate visual performance under tinted eyewear: one focused on small color difference, assessed by reaction time and accuracy of spatial detection, and the other on large color differences, evaluated through viewing distance. By comparing the experimental data with model predictions, this study aims to provide a deeper understanding of the color perception changes caused by tinted eyewear, particularly in relation to the accuracy of spatial detection and its dependence on color difference.
10:10IQSP-246
Rendering emissive colors in HDR scenes: The role of reference-white placement and highlight tone mapping, Alireza Rabbanifar, Rochester Institute of Technology (US); Mekides Abebe, Rochester Institute of Technology (US); Mark Fairchild, Rochester Institute of Technology (US); Soroush Shahbaznejad, Rochester Institute of Technology (US) [view abstract]
Determining a scene s reference white is fundamental to setting the viewer s adaptation state and shaping perceived color appearance. In most color-appearance frameworks, reference white corresponds to diffuse white the luminance of a perfect diffuser. Rendering a high-dynamic-range (HDR) scene on an HDR display therefore requires careful placement of this diffuse-white point. Standards such as ITU-R BT.2084 (PQ) recommend mapping diffuse white to 203 nits, placing all brighter scene content into the restricted highlight headroom between 203 nits and the display peak. However, HDR scenes with strong specular reflections or emissive light sources often far exceed this range, increasing the risk of highlight compression or clipping.In this work, we capture a controlled HDR scene containing both reflective and emissive color targets and render it on a professional HDR mastering display while systematically varying the displayed diffuse-white luminance. We examine how these choices influence the reproduction of emissive targets, whose luminance may sit well above diffuse white. To compare tone-mapping strategies, we apply custom operators either globally or only within the highlight headroom. Objective accuracy is evaluated by computing ?E00 between displayed patches and their true scene-referred values. To assess perceptual outcomes, observers first adapt to the real scene and then rate the fidelity of each display rendering.Overall, the results show how reference-white placement and highlight tone mapping jointly affect the color accuracy and perceptual reproduction of emissive elements in HDR scenes.
Skintone Capture and IQ
Session Chair: Chaker Larabi, Universite de Poitiers
11:00 - 12:20
Grand Peninsula B
11:00IQSP-247
Toward fair and accurate camera testing: Validation of skin tone test charts with real human data, Megan Borek, Imatest, LLC (US); Amelia Limbocker, Imatest, LLC (US); Ellis Monk, Harvard University (US) [view abstract]
Accurate reproduction of diverse skin tones remains a persistent challenge for consumer cameras, with shortcomings in automatic exposure and white balance often leading to biased results. To address this, we are developing skin tone test charts designed to better represent real human skin across the full range of the Monk Skin Tone Scale. This work validates these charts using real human data collected through multiple methods. First, survey responses capture user perspectives on how their skin tones are represented in smartphone images and what they expect from their devices. Second, controlled photographic sessions with participants across the scale provide image data under varied lighting conditions and multiple exposure strategies, including gray card, spot metering, and bracketing, alongside participant-selected preferred exposures. Finally, spectral reflectance measurements from skin regions supplement the imaging data with objective ground truth. Together, these datasets allow us to evaluate and tune test chart behavior so that color and exposure responses align more closely with real skin. This validation represents a step toward fairer, more accurate camera testing standards, with implication for consumer photography as well as applications in medical, automotive, and security images.
11:20IQSP-248
Perceptual thresholds of facial lighting chromaticity in virtual environments, Xinmiao Zhang, Rochester Institute of Technology (US); Sofie Herbeck, Rochester Institute of Technology (US); Christopher Thorstenson, Rochester Institute of Technology (US) [view abstract]
Lighting chromaticity plays a critical role in the visual perception of rendered content embedded within virtual scenes. Consequently, much effort is made to reduce differences in lighting chromaticity between these, to avoid an unnatural combined appearance. However, it is not currently known "how" different these lighting conditions can be before people can notice them, or before it becomes detrimental to visual appearance. In this study, three psychophysical tasks are employed to assess participants' perception of simulated lighting conditions applied separately to rendered virtual backgrounds and virtual objects, with a focus on stimuli comprising human faces. The tasks assessed the influence of object characteristics (variable skin tone), and differences in lighting chromaticity (between scenes and objects) on visual assessments for perceived lighting matches, mismatches, and preferences. Results revealed that both object and lighting characteristics significantly influenced each perceptual judgment, in different ways. Chromaticity matches, mismatches, and preferences for facial stimuli depended on the scene chromaticity and skin tone but their patterns varied across tasks. The current work can provide guidance for virtual rendering based on visual perception of simulated lighting differences.
11:40IQSP-249
IQSP KEYNOTE: Preferred skin color rendition for self-representative faces and avatars in augmented reality, Dara Dimoff, Rochester Institute of Technology (US); Susan Farnand, Rochester Institute of Technology (US); Christopher Thorstenson, Rochester Institute of Technology (US) [view abstract]
Augmented reality (AR) is a technology that enables humans to superimpose visual elements over the real world. One contemporary approach to AR technology is optical see-through AR (OST-AR) in which the physical world is viewed through a transparent medium that displays graphical elements. This model faces a major challenge in color appearance: its additive color mixing creates a bleed-through effect in which the environment blends with the appearance of the graphical elements especially in environments with high luminance or with dark graphical elements creating a translucent appearance. Notably, graphical human faces having darker skin tones appear more transparent than those with lighter skin tones, introducing both perceptual and social challenges.In this work, a psychophysical study assesses observers preferred renderings of skin tones in OST-AR. Observers are given a task to adjust the lightness and chroma of faces superimposed by AR glasses in various illumination conditions. This study focuses on the color adjustments made to pictures of real faces vs. their corresponding digital avatars, and stimuli representing the observer vs. zero-acquaintance targets.
Imager Simulation and Modeling
Session Chair: Bo Ding, Snap, Inc.
15:30 - 17:30
Grand Peninsula B
15:30IQSP-250
Image sensor noise model for image system simulation, Norman Koren, Imatest, LLC (US) [view abstract]
We present an image sensor noise model, which is part of a complete image system simulation that includes image generation, lens degradations, and ISP (Image Signal Processing), and can produce classic measurements (SFR, noise, etc.) as well as the new information metrics (information capacity, SNRi, etc.).The noise model is derived from a classic Photon Transfer Curve (PTC) obtained from one or at most two raw (undemosaiced) images of a high dynamic range grayscale test chart. Image sensor noise is composed of three factors. 1. Dark noise, which includes electronic noise, dark current noise, and DSNU fixed-pattern noise. It is independent of amplitude. 2. Photon shot noise, which varies with the square root of the amplitude, and 3. PRNU fixed-pattern noise, which varies linearly with amplitude.The coefficients for the three factors are determined using a Levenberg Marquardt optimization that provides an extremely close fit between the data to the measured PTC. The coefficients can also be derived from EMVA 1288 measurements, which are more detailed, but require a large number of images to acquire.We show how the model can predict performance over a wide range of conditions, most importantly, for low light.
15:50IQSP-251
End-to-end CIS digital twin using lens geometry, spectral ray tracing and pixel modeling, Jeongyong Shin, Samsung Electronics (Republic of Korea); Seonghyeon Kang, Samsung Electroncis (Republic of Korea); Sangmin Kim, Samsung Electronics (Republic of Korea); Jinhee Kim, Ansys (Republic of Korea); Julien Muller, Ansys (France); Kam Chow, Ansys (Taiwan (Greater China)); Jeongwook Lee, Samsung Electronics (Republic of Korea); Sung-Su Kim, Samsung Electronics (Republic of Korea); Yitae Kim, Samsung Elctronics (Republic of Korea) [view abstract]
This paper presents the development process of an End-to-End Digital Twin system for CMOS Image Sensors (CIS) using Ansys Zemax, Speos, and Lumerical tools. The lens shape and material properties of an actual smartphone camera were extracted using Zemax, and spectral ray tracing simulations were conducted in Speos to calculate the irradiance after light passed through the lens. Subsequently, Lumerical was employed to precisely model the pixel-level quantum efficiency (QE) as a function of wavelength and optical field, enabling the generation of a fully simulation-based digital image. A verification environment based on the Cornell Box was built for real-world measurements, and the Digital Twin simulation results were compared against actual captured images in terms of edge spread function (ESF), chromatic aberration, distortion, and chromaticity (CIE 1931). The comparison demonstrated the reliability and accuracy of the proposed model.
16:10IQSP-252
MTF and aliasing characterization of photon router CMOS image sensor, Yuyao Chen, Omnivision Technologies Inc. (US); Yahya Mohtashami, Omnivision Technologies Inc. (US); Zhiqiang Lin, Omnivision Technologies Inc. (US); Dyson (HsinChih) Tai, Omnivision Technologies Inc. (US); Eiichi Funatsu, Omnivision Technologies Inc. (US) [view abstract]
Over recent decades, shrinking the pixel size of image sensors has improved image resolution. Achieving a high signal-to-noise ratio with small pixel sensors is challenging. To address the challenge, some diffraction-based photon routing (PR) technologies were proposed to boost sensor quantum efficiency. However, full-wave simulations reveal that PR exhibits lower modulation transfer function (MTF) values compared to microlens (ML), indicating a reduced resolution. This work develops a general MTF model for Bayer-pattern sensors that accurately fits both ML and PR MTF curves. We then quantitatively characterize aliasing effects based on ML and PR system MTFs. Using a camera simulator with Siemens star patterns, we demonstrate that PR images exhibit less aliasing than the ML, consistent with lower aliasing components in PR s system MTF. Finally, by tuning image signal processing (ISP) sharpening parameters, we show that PR s resolution is improved to approach that of ML. This suggests a practical pathway to improve resolution loss while leveraging PR s SNR advantages.
WEDNESDAY 4 MARCH 2026
IQ Standards I
Session Chair: Peter Burns, Burns Digital Imaging LLC
08:30 - 10:30
Harbour A
08:30IQSP-253
Python implementation of SFRMAT5, Robin Jenkin, NVIDIA (US); Vijayalakshmi Sajjanar, KLE Technological University (India); Aditya Khatawkar, KLE Technological University (India); Ayaan Dhamnekar, KLE Technological University (India); Apeksha Bannigidad, KLE Technological University (India); Nishchay Goankar, KLE Technological University (India) [view abstract]
ISO 12233 details the use of the sloping edge technique, which amongst others, is used to estimate the spatial frequency response (SFR) of imaging systems. SFRMAT, written by Burns, has existed for in excess of 25 years as a practical implementation of the ISO12233 standard. As such, it has proven to be an excellent and easy-to-use resource for deriving the SFR of systems and is currently in Version 5 of its MATLAB implementation.This paper presents a Python-based open-source implementation of SFRMAT5 with duplicate capabilities and documents the process of conversion and testing of its functionality. The intent is to make SFRMAT more accessible to the wider imaging community as the popularity of Python as a programming language has increased significantly in recent years. The implementation was intentionally organized to mirror that of the original MATLAB function to make ongoing maintenance as easy as possible and to facilitate the coexistence of both versions. The resulting package is modular with a CLI, GUI, and headless support. Unit and system level testing demonstrates maximum absolute errors of 0.001 SFR at just two frequency positions in a single color channel when compared to the MATLAB implementation using the same input. Ongoing work will continue to eliminate these differences. Some difficulties associated with conversion of the function are documented and examples of operation are given.
08:50IQSP-254
From centroid to low-pass edge fitting in ISO 12233 eSFR: Accuracy and impact on digital imaging information metrics, Sarah Kerr, Imatest LLC (US); Norman Koren, Imatest LLC (US) [view abstract]
Edge localization estimation methods play a critical role in ISO 12233 eSFR analysis, influencing both sharpness results and downstream information capacity metrics. This paper evaluates the accuracy of the standard centroid method relative to a low-pass filter approach across cameras and ISO ranges. Localization errors are benchmarked against low-noise ground truth, and their propagation to eSFR results, information capacity, and SNRi metrics is quantified. Findings show that centroid fitting introduces angular bias under noise, leading to blurrier effective responses, while low-pass filtering maintains robust accuracy. These results highlight an underexplored source of error in standards-based image quality analysis and provide a foundation for improved methods. The approach will be extended toward matched filtering, enabling a closer alignment between edge analysis, information-theoretic models, and emerging metrics such as those in ISO 23654 (Digital Imaging Information Metrics).
09:10IQSP-255
Measurement of the modulation transfer function of video endoscopes, Quanzeng Wang, U.S. Food and Drug Administration (US) [view abstract]
Endoscopes are essential for surgery and disease diagnosis, including early cancer detection. Their optical performance, particularly resolution, is critical and can be assessed using metrics like the modulation transfer function (MTF). The ISO 8600-5: 2020 standard introduced a method for measuring endoscope MTF but excludes opto-electronic video endoscopes, the largest group. This study aims to expand the methods for measuring endoscope MTFs to include video endoscopes by optimizing the MTF test method and addressing factors like luminance, auto gain control, gamma correction, image enhancement, compression, and region of interest size. The study also examines the relationships among spatial frequencies in different imaging spaces and their effects on MTF interpretation. Our goal is to improve the accuracy and applicability of MTF measurement methods across more endoscopic devices.
IQ Standards II
Session Chair: Meg Borek, Imatest
11:00 - 12:20
Harbour A
11:00IQSP-256
Depth-aware assessment of spatial frequency response in natural scenes, Sara Lee, Samsung Electronics (Republic of Korea); Yu Gyeong Lee, Samsung Electronics (Republic of Korea); Seungwan Jeon, Samsung Electronics (Republic of Korea); Subin Han, Samsung Electronics (Republic of Korea); Junho Han, Samsung Electronics (Republic of Korea); DongOh Kim, Samsung Electronics (Republic of Korea); KiChul Park, Samsung Electronics (Republic of Korea); Sung-Su Kim, Samsung Electronics (Republic of Korea) [view abstract]
The spatial frequency response (SFR) has long been a crucial metric for evaluating imaging quality, particularly in camera performance assessment. However, the constraints of chart-based assessment limited the evaluation of natural scenes, making it challenging to evaluate resolution accurately in real-world environments. Notably, the development of the natural scene spatial frequency response (NS-SFR) has enabled resolution evaluation from natural scenes, extending its utility to diverse applications. Nevertheless, existing NS-SFR methods have been limited to two-dimensional analysis, neglecting depth-dependent behaviors such as variations in sharpness across focal planes. To address this limitation, we propose a depth-aware extension of NS-SFR, integrating depth dimension into modulation transfer function (MTF) analysis, and establish a model of the depth-MTF relationship that derives a representative MTF value for a single image s resolution. Our approach extends conventional planar NS-SFR analysis into a 3D depth-augmented framework that accounts for depth-dependent variations in MTF. Also our results suggest that our approach enables a more resilient and informative methodology for accurate cross-sensor comparison, yielding predictions that show a reasonable correspondence with resolution tendencies observed in natural scenes, while enhancing robustness under varying illumination.
11:20IQSP-257
IQSP KEYNOTE: Acceptance levels for image quality factors, [view abstract]
To determine the lowest light level for which a digital camera still delivers images that are acceptable requires acceptance thresholds for all related image quality factors. ISO 19093 [1] describes these factors and how they can be measured. The acceptance thresholds however may depend on the application for which the images were captured and on peoples individual tolerance for the degradation of the different image quality factors. In order to generate a standard set of tolerance levels for photographic applications a psychophysical experiment was performed as described in this paper. A group of 23 image quality experts participated to begin followed by 16 people who did not have a specific experience in imaging.
11:50IQSP-258
Introduction to ISO/DIS 21496-1, digital photography Gain map metadata for image conversion Part 1: Dynamic range conversion, Nicolas Bonnier, Apple (US); Paul Hubel, Apple (US) [view abstract]
ISO 21496-1 standardizes how to convert between standard dynamic range (SDR) and high dynamic range (HDR) images using gain maps. A gain map is a new type of metadata that locally adjusts brightness in the image, allowing efficient creation of alternate SDR/HDR versions without duplicating full image data. The standard is important because it eliminates fragmented, proprietary methods from different platforms, ensuring HDR photos display consistently across devices and software. By enabling interoperability, efficiency, and high visual quality, ISO 21496-1 supports seamless sharing, enhances user experience, and accelerates adoption of HDR imaging worldwide.
Automotive Imaging Performance I (Joint Session with Autonomous Vehicles and Machines)
Session Chair: Elaine Jin
15:30 - 16:30
Grand Peninsula G
15:30AVM-113
Balancing exposure vs. resolution in high-speed ADAS imaging, Gabriel Bowers, Mobileye (France); Uwe Artmann, Image Engineering (Germany); Max Gade, Image Engineering (Germany) [view abstract]
The automotive industry has made significant strides in improving camera technologies for ADAS and autonomous vehicles, with a strong focus on increasing resolution, achieving flicker-free dynamic range, and improving low light sensitivity. These improvements are challenged when driving at increasing speeds, as shorter exposures are needed to detect nearby objects at the same resolution. A representation of such effects is often missing from the camera IQ tests. However, such analysis can be valuable as it can better clarify the system design trade-off between caSummary Imagemera specifications (e.g., resolution, lens aperture) and exposure control design. This paper will provide an attempt to highlight how this analysis of a system implementation can be achieved in lab settings.
15:50AVM-114
brilliantISP: An open source HDR ISP for research, Brian Deegan, University of Galway (Ireland) [view abstract]
BrilliantISP: An Enhanced HDR Image Signal Processing Pipeline Abstract This paper presents BrilliantISP, an enhanced high dynamic range (HDR) image signal processing pipeline designed to advance computational photography capabilities. Building upon the foundation of infiniteISP, FastOpenISP, and OpenISP, our implementation introduces several key innovations to address limitations in existing open-source ISP solutions. The enhanced pipeline incorporates a novel decompanding function that effectively linearizes companded sensor data, enabling more accurate downstream processing. We have integrated Durand's HDR tone mapping algorithm, providing superior dynamic range compression while preserving local contrast and detail visibility. The system features modified bit depth handling throughout the pipeline to maintain precision during HDR processing operations. Significant performance optimizations have been achieved through algorithmic improvements and execution time optimization, with comprehensive debug logging capabilities for development and research applications. The modular architecture supports flexible configuration through YAML parameter files, enabling rapid prototyping and experimentation. Current development focuses on implementing HDR multicapture merge functionality and lens shading correction algorithms. Preliminary results demonstrate improved image quality metrics compared to baseline implementations, particularly in high contrast scenes. The open-source nature of BrilliantISP facilitates reproducible research and collaborative development in computational photography. This work contributes to the growing ecosystem of open-source ISP solutions, providing researchers and developers with enhanced tools for HDR image processing applications in both academic and commercial contexts.
16:10AVM-115
Information-based dynamic range, [view abstract]
We present a new approach to measuring camera dynamic range and low-light performance based on C4 information capacity, which is measured directly from ISO 12233-standard 4:1 contrast slanted edges. Our initial technique involves photographing a test chart that contains 4:1 slanted edges over an extremely wide range of exposures, from or second (where the brighter side of the edge saturates) to 1/2000 or 1/4000 second, where the image appears nearly black, but a noisy edge is still present. The major advantages of this method are1. Dynamic range limits are based on an actual performance metric (C4) rather than SNR, which is only one of the factors that contributes to camera performance.2. C4 correctly handles performance degradation due to stray light.In the final paper we will discuss new techniques, still under development, for facilitating the measurement.
Automotive Imaging Performance II (Joint Session with Autonomous Vehicles and Machines)
Session Chair: Elaine Jin
16:30 - 17:30
Grand Peninsula G
16:30AVM-116
A method for calculating NIR bandpass-adjusted optical densities for better matching common standard test chart specifications., Christian Taylor (US); Amelia Limbocker (US) [view abstract]
Near-infrared (NIR) imaging is now prevalent in machine vision, automotive, and biomedical applications, but most step-chart definitions were created for visible imaging. Many standards assume visible lighting conditions and only consider IR-blocking, so NIR-sensitive and RGB+NIR cameras are not adequately addressed. This leads to charts whose nominal densities don't produce results as intended. We present a camera-matched methodology for designing NIR test charts whose optical densities (ODs) align with the effective bandpass of a specific camera. First, we estimate the camera illumination optics bandpass by measuring camera spectral responsivity and the measured illuminant spectrum at the sample plane. Next, we measure transmittance and/or reflectance spectra of candidate chart materials via spectrophotometry and predict their camera-effective ODs as band-integrated log-ratios. We then select step values to meet the target OD values. Validation is performed by imaging the manufactured chart in RAW, applying linearization, and comparing measured image OD to predictions and to a reference spectrophotometer integrated over the same band. The framework supports transmission and reflectance charts, mono and RGB-NIR sensors, and bands spanning ~780 1100 nm. We report a practical design recipe and guidelines for dynamic-range coverage and repeatability, enabling camera-aware NIR chart optimization rather than one-size-fits-all designs.
16:50AVM-117
Circular-edge SFR for camera image quality assessment: Theory, implementation, and validation, Xingbo Wang, Shanghai Yanding Tech. Co., Ltd (China (Mainland)); Sangkyu Yang, CIZEN TECH Co., Ltd. (Republic of Korea) [view abstract]
lang="EN-US">Spatial Frequency Response (SFR), typically measured by the ISO 12233 slanted-edge method, is a standard metric for quantifying image sharpness. Despite its robustness, the method is limited by orientation dependence, sensitivity to tilt angle, and distortion-induced edge curvature. Circular-edge SFR has been proposed as an alternative, but its properties and applicability remain largely unexplored. lang="EN-US" style="font-family: "Calibri", sans-serif; font-size: 11pt; line-height: 115%">We present an implemented circular-edge SFR algorithm and evaluate it through both simulation and physical experiments. Simulations incorporate diffraction, aberrations, sensor sampling, and distortion models, with ground-truth MTFs enabling accuracy assessment. Physical validation employs automotive camera modules. Preliminary results demonstrate strong agreement between circular-edge and slanted-edge SFR, supporting the feasibility of the approach. Further analyses examine robustness under distortion and orientation variation, with the goal of assessing circular-edge SFR as a complementary or alternative standard.
17:10AVM-118
Same scene, different pipeline: ISP impact on automotive detection at range, Tejus Vijayakumar, University of Limerick (Ireland); Ciar n Eising, University of Limerick; Brian Deegan, University of Galway; Patrick Denny, University of Limerick [view abstract]
THURSDAY 5 MARCH 2026
Perception and Image Quality I (Joint Session with Human Vision and Electronic Imaging
Session Chair: Lukas Krasula, Adobe
08:30 - 09:40
Grand Peninsula A
08:30HVEI-233
Ongoing activities in the Video Quality Experts Group (VQEG), Kjell Brunnstroem, RISE Research Institutes of Sweden AB (Sweden); Ioannis Katsavounidis, Meta (US) [view abstract]
The Video Quality Experts Group (VQEG) was established to unite experts in subjective and objective video quality assessment. Since its first meeting in 1997, VQEG has focused on advancing video quality assessment methods and validating new objective quality metrics for standardization. VQEG is open to all interested parties without any fees or membership requirements, and its activities are documented and submitted to relevant ITU Study Groups or published in scientific journals.Examples of recent progress include: 1) The Immersive Media Group (IMG) has been developing a test plan, based on a large-scale cross-lab experiment, to evaluate the Quality of Experience (QoE) of immersive interactive communication systems, in collaboration with ITU-T, resulting in the Recommendation ITU-T Rec. P1321. 2) The 5G Key Performance Indicators (5GKPI) group is studying the relationship between 5G/6G network performance and video service QoE. They are preparing a VQEG Whitepaper on QoE management in telecommunication networks, which will be published soon.
08:50HVEI-234
IQSP KEYNOTE: The influence of image semantic complexity on the performance of image quality metrics, Marius Pedersen, Norwegian University of Science and Technology (Norway); Peiyuan Zhang, Zhejiang Wanli University (China (Mainland)); Xinwei Liu, Zhejiang Wanli University (China (Mainland)); Sophie Triantaphillidou, Norwegian University of Science and Technology (Norway) [view abstract]
Image quality assessment has been a longstanding area of research, with significant efforts dedicated to developing objective metrics that can reliably predict perceived image quality. While numerous image quality metrics have been proposed, ranging from traditional handcrafted approaches to modern machine learning-based models, their evaluation typically relies on statistical comparisons with subjective human ratings where correlation is reported as the main way to assess their performance. In this study, we explore an additional dimension in image quality evaluation: the impact of image semantic complexity on metric performance. Specifically, we hypothesize that the number of distinct semantic categories within an image influences the accuracy of image quality metrics. We evaluate 8 state-of-the-art image quality metrics across 2 benchmark datasets, analyzing performance variations with respect to image semantic complexity (category count), based on two vision-language models. Our findings reveal that for some image quality metrics there is an impact of semantic complexity and outliers. This could suggest that content-aware evaluation may be crucial for future image quality research.
09:20HVEI-235
JIST-first-2025-007: Quality evaluation of contrast-enhanced images: Central Asians' perspectives, Altynay Kadyrova, KIMEP University; Marius Pedersen, Norwegian University of Science and Technology (Norway) [view abstract]
Culture can play a significant role in evaluating image quality. Therefore, we considered one of the least studied origin regions of observers, the impact of Central Asian culture on image quality evaluation. More specifically, we investigated how they evaluate the quality of contrast-enhanced images. We found that observers evaluations vary and can be divided into groups. These groups may have their individual preferences for the quality of contrast-enhanced images. Therefore, the personalization factor should be incorporated into the quality evaluation of (contrast) enhanced images. Furthermore, we obtained similar results with existing studies that current image quality metrics might not be enough to evaluate the quality of enhanced images. In addition, we introduced the Central Asian Contrast-Enhanced Image Quality Dataset (CACEIQD). Our dataset can be helpful for future research in the field of enhanced image quality evaluation.
Perception and Image Quality II (Joint Session with Human Vision and Electronic Imaging)
Session Chair: Lukas Krasula, Adobe
09:40 - 10:40
Grand Peninsula A
09:40HVEI-236
Method and findings for determining the just noticeable difference (JND) for an image sharpness metric, Tero Vuori, Microsoft (Finland); Jukka-Pekka Raunio, Microsoft (Finland); Ari Partinen, Microsoft (Finland) [view abstract]
In camera product development, where the goal is to achieve the best possible image quality and user experience, it is necessary to use both objective and subjective test methods. Both methods have a long tradition and their own advantages and disadvantages. Objective image quality measures are fast and efficient. They form the basis for, for example, camera product comparisons daily basis. Based on the results provided by the measure, it is possible to rank any camera products easily. However, how big is the noticeable difference between two products for a user if such an objective measure is used? When is the difference significant? Or does the user notice the measured difference at all? In this study, we wanted to get answers to these questions for our acutance measure which is used daily basis. We conducted numerous subjective tests in a controlled lab with carefully chosen stimuli. Based on these subjective results, we calculated the corresponding Just Noticeable Difference (JND) values for our acutance measure. This study presents methods and results for finding JND values for an objective acutance measure that can be more broadly generalized to all objective acutance measures and, in terms of the method, to all objective measures. Our result shows that one JND corresponds to about 0.02 acutance units which corresponds to a perceptual difference of approximately 2% in acutance units.
10:20HVEI-238
On a perceptually accurate visual noise metric for HDR imaging: Accounting for luminance adaptation and gradient effects, Hugo Masson, DXOMARK Image Labs (France); Francois-Xavier Thomas, DXOMARK Image Labs (France); Claudio Greco, DXOMARK Image Labs (France); Daniela Carfora Ventura, DXOMARK Image Labs (France); Mauro Patti, DXOMARK Image Labs (France); Benoit Pochon, DXOMARK Image Labs (France); Hoang-Phi Nguyen, DXOMARK Image Labs (France); Laurent Chanas, DXOMARK Image Labs (France); Frederic Guichard, DXOMARK Image Labs (France) [view abstract]
Current visual noise 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 observer s luminance adaptation, 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 despite appearing visually clean.This paper proposes a new direction for Visual Noise algorithms and lays the groundwork for future research. It presents findings on: A new HDR ruler for visual noise assessment; The impact of various factors (CSF, MTF, HPF, gradient correction, edge windowing) on algorithm performance; Evaluation of different color spaces for computing visual noise metrics