CameraVDP: Perceptual Display Assessment with Uncertainty Estimation via Camera and Visual Difference Prediction

• URL: http://arxiv.org/abs/2509.08947v2
• Date: Sun, 21 Sep 2025 21:34:01 GMT
• Title: CameraVDP: Perceptual Display Assessment with Uncertainty Estimation via Camera and Visual Difference Prediction
• Authors: Yancheng Cai, Robert Wanat, Rafal Mantiuk,
• Abstract summary: We propose a camera-based reconstruction pipeline with a visual difference predictor.<n>The reconstruction pipeline combines HDR image stacking, MTF inversion, vignetting correction, geometric undistortion, homography transformation, and color correction.<n>We validate the proposed CameraVDP framework through three applications: defective pixel detection, color awareness, and display non-uniformity evaluation.
• Score: 4.2330023661329355
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate measurement of images produced by electronic displays is critical for the evaluation of both traditional and computational displays. Traditional display measurement methods based on sparse radiometric sampling and fitting a model are inadequate for capturing spatially varying display artifacts, as they fail to capture high-frequency and pixel-level distortions. While cameras offer sufficient spatial resolution, they introduce optical, sampling, and photometric distortions. Furthermore, the physical measurement must be combined with a model of a visual system to assess whether the distortions are going to be visible. To enable perceptual assessment of displays, we propose a combination of a camera-based reconstruction pipeline with a visual difference predictor, which account for both the inaccuracy of camera measurements and visual difference prediction. The reconstruction pipeline combines HDR image stacking, MTF inversion, vignetting correction, geometric undistortion, homography transformation, and color correction, enabling cameras to function as precise display measurement instruments. By incorporating a Visual Difference Predictor (VDP), our system models the visibility of various stimuli under different viewing conditions for the human visual system. We validate the proposed CameraVDP framework through three applications: defective pixel detection, color fringing awareness, and display non-uniformity evaluation. Our uncertainty analysis framework enables the estimation of the theoretical upper bound for defect pixel detection performance and provides confidence intervals for VDP quality scores.

Related papers

• Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models [58.98742597810023]
  Vision models have to behave in a robust way to disturbances such as noise or blur.<n>This paper studies two datasets of blur corruptions, which we denote OpticsBench and LensCorruptions.<n> Evaluations for image classification and object detection on ImageNet and MSCOCO show that for a variety of different pre-trained models, the performance on OpticsBench and LensCorruptions varies significantly.
  arXiv  Detail & Related papers  (2025-04-25T17:23:47Z)
• Möbius Transform for Mitigating Perspective Distortions in Representation Learning [43.86985901138407]
  Perspective distortion (PD) causes unprecedented changes in shape, size, orientation, angles, and other spatial relationships in images. We propose mitigating perspective distortion (MPD) by employing a fine-grained parameter control on a specific family of M"obius transform. We present a dedicated perspectively distorted benchmark dataset, ImageNet-PD, to benchmark the robustness of deep learning models against this new dataset.
  arXiv  Detail & Related papers  (2024-03-07T15:39:00Z)
• Single-image camera calibration with model-free distortion correction [0.0]
  This paper proposes a method for estimating the complete set of calibration parameters from a single image of a planar speckle pattern covering the entire sensor. The correspondence between image points and physical points on the calibration target is obtained using Digital Image Correlation. At the end of the procedure, a dense and uniform model-free distortion map is obtained over the entire image.
  arXiv  Detail & Related papers  (2024-03-02T16:51:35Z)
• ColorVideoVDP: A visual difference predictor for image, video and display distortions [51.29162719944865]
  metric is built on novel psychophysical models of chromatic contrast sensitivity and cross-channel contrast masking. It accounts for the viewing conditions, geometric, and photometric characteristics of the display. It was trained to predict common video streaming distortions and 8 new distortion types related to AR/VR displays.
  arXiv  Detail & Related papers  (2024-01-21T13:16:33Z)
• Inertial Guided Uncertainty Estimation of Feature Correspondence in Visual-Inertial Odometry/SLAM [8.136426395547893]
  We propose a method to estimate the uncertainty of feature correspondence using an inertial guidance. We also demonstrate the feasibility of our approach by incorporating it into one of recent visual-inertial odometry/SLAM algorithms.
  arXiv  Detail & Related papers  (2023-11-07T04:56:29Z)
• Differentiable Display Photometric Stereo [15.842538322034537]
  Photometric stereo leverages variations in illumination conditions to reconstruct surface normals. We present differentiable display photometric stereo (DDPS), addressing the design of display patterns. DDPS learns the display patterns that yield accurate normal reconstruction for a target system in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-06-23T07:05:08Z)
• SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing Image Dehazing Using Satellite-to-Ground NDVI Knowledge [15.389028295437974]
  In our industrial deployment scenario based on remote sensing (RS) images, the quality of image dehazing directly affects the grade of our crop identification and growth monitoring products. In this paper, we design a new objective metric for RS image dehazing evaluation.
  arXiv  Detail & Related papers  (2023-06-09T22:29:42Z)
• Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration [66.33746403815283]
  We propose a scene-adaptive infrared and visible image registration. We employ homography to simulate the deformation between different planes. We propose the first ground truth available misaligned infrared and visible image dataset.
  arXiv  Detail & Related papers  (2023-04-12T06:49:56Z)
• How to Calibrate Your Event Camera [58.80418612800161]
  We propose a generic event camera calibration framework using image reconstruction. We show that neural-network-based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras.
  arXiv  Detail & Related papers  (2021-05-26T07:06:58Z)
• Wide-angle Image Rectification: A Survey [86.36118799330802]
  wide-angle images contain distortions that violate the assumptions underlying pinhole camera models. Image rectification, which aims to correct these distortions, can solve these problems. We present a detailed description and discussion of the camera models used in different approaches. Next, we review both traditional geometry-based image rectification methods and deep learning-based methods.
  arXiv  Detail & Related papers  (2020-10-30T17:28:40Z)
• D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry [57.5549733585324]
  D3VO is a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. We model the photometric uncertainties of pixels on the input images, which improves the depth estimation accuracy.
  arXiv  Detail & Related papers  (2020-03-02T17:47:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.