Abstract While existing video and image quality datasets have extensively studied natural videos and traditional distortions, the perception of synthetic content and modern rendering artifacts remains underexplored. We present a novel video quality dataset focused on distortions introduced by advanced rendering techniques, including neural supersampling, novel‐view synthesis, path tracing, neural denoising, frame interpolation, and variable rate shading. Our evaluations show that existing full‐reference quality metrics perform sub‐optimally on these distortions, with a maximum Pearson correlation of 0.78. Additionally, we find that the feature space of pre‐trained 3D CNNs aligns strongly with human perception of visual quality. We propose CGVQM, a full‐reference video quality metric that significantly outperforms existing metrics while generating both per‐pixel error maps and global quality scores. Our dataset and metric implementation is available at https://github.com/IntelLabs/CGVQM .

A faithful reproduction of gloss is inherently difficult because of the limited dynamic range, peak luminance, and 3D capabilities of display devices. This work investigates how the display capabilities affect gloss appearance with respect to a real-world reference object. To this end, we employ an accurate imaging pipeline to achieve a perceptual gloss match between a virtual and real object presented side-by-side on an augmented-reality high-dynamic-range (HDR) stereoscopic display, which has not been previously attained to this extent. Based on this precise gloss reproduction, we conduct a series of gloss matching experiments to study how gloss perception degrades based on individual factors: object albedo, display luminance, dynamic range, stereopsis, and tone mapping. We support the study with a detailed analysis of individual factors, followed by an in-depth discussion on the observed perceptual effects. Our experiments demonstrate that stereoscopic presentation has a limited effect on the gloss matching task on our HDR display. However, both reduced luminance and dynamic range of the display reduce the perceived gloss. This means that the visual system cannot compensate for the changes in gloss appearance across luminance (lack of gloss constancy), and the tone mapping operator should be carefully selected when reproducing gloss on a low dynamic range (LDR) display.

Reproduction of occlusions and opaque surfaces are the major challenges of additive optical see-through (OST) displays. This is because the user of an OST display sees a linear mixture of display and environment light, which creates an impression of transparency unless the displayed color is sufficiently bright. The primary goal of this work is to determine how bright a displayed surface needs to be in relation to environment light to be perceived as opaque. We test multiple factors that could affect the perception of opacity: background luminance, contrast, spatial frequency, and accommodation depth in foveal vision. The subjective results, collected on a high-dynamic-range multi-focal stereo display, indicate that a virtual object needs to be, on average, 60 times brighter than the background environment light to be perceived as opaque. A higher contrast of the texture of the virtual object and a background that is out of focus can reduce the required luminance ratio. We demonstrate that a model of visual perception based on Weber’s law and accounting for contrast masking and defocus blur can predict the experimental data with an averaged prediction error of 8.29%. Existing perceptual image difference metrics (PSNR, FovVideoVDP and HDR-VDP-3) can also predict the effect of major factors, but with lower accuracy (e.g. prediction error of 34% for PSNR with PU21 encoding).

The current Distribution Grid Automation (DGA) Systems are being heavily dependent on the Information and Communication Technologies (ICT) infrastructure for its proper operation. The DGA architectures are predominantly centralized and usually deployed on a dedicated hardware. This increases the risk of blackouts under a coordinated cyber-physical attack. The compromise of the dedicated hardware that hosts the central coordinator of the DGA automation results in a blackout. Though many countermeasures have already been proposed for tackling different types cyber and physical attacks on the ICT infrastructure, very few measures have been proposed to ensure the availability of the grid operation functions, even when it is compromised. This study proposes an automatic, distributed approach based on Blockchain and Smart Contract that ensures the availability of the core DGA functions even if the central coordinator that operates the grid is compromised. This is done by virtualizing and migrating/re-initialising these functions from the dedicated hardware that was compromised to another. Additionally, a Multi-Attribute Decision Making based method is incorporated into the Smart Contract that helps in selection of the optimal hardware that can host the function considering its limitations (hardware and software). Finally, a proof of concept implementation of the proposed solution is presented that utilizes the Calvin IoT (Internet of Things) platform, Flow programming tool and Hyperledger fabric and its performance is evaluated.

When the rendering budget is limited by power or time, it is necessary to find the combination of rendering parameters, such as resolution and refresh rate, that could deliver the best quality. Variable-rate shading (VRS), introduced in the last generations of GPUs, enables fine control of the rendering quality, in which each 16×16 image tile can be rendered with a different ratio of shader executions. We take advantage of this capability and propose a new method for adaptive control of local shading and refresh rate. The method analyzes texture content, on-screen velocities, luminance, and effective resolution and suggests the refresh rate and a VRS state map that maximizes the quality of animated content under a limited budget. The method is based on the new content-adaptive metric of judder, aliasing, and blur, which is derived from the psychophysical models of contrast sensitivity. To calibrate and validate the metric, we gather data from literature and also collect new measurements of motion quality under variable shading rates, different velocities of motion, texture content, and display capabilities, such as refresh rate, persistence, and angular resolution. The proposed metric and adaptive shading method is implemented as a game engine plugin. Our experimental validation shows a substantial increase in preference of our method over rendering with a fixed resolution and refresh rate, and an existing motion-adaptive technique.

With well-established methods for producing photo-realistic results, the next big challenge of graphics and display technologies is to achieve perceptual realism --- producing imagery indistinguishable from real-world 3D scenes. To deliver all necessary visual cues for perceptual realism, we built a High-Dynamic-Range Multi-Focal Stereo Display that achieves high resolution, accurate color, a wide dynamic range, and most depth cues, including binocular presentation and a range of focal depth. The display and associated imaging system have been designed to capture and reproduce a small near-eye three-dimensional object and to allow for a direct comparison between virtual and real scenes. To assess our reproduction of realism and demonstrate the capability of the display and imaging system, we conducted an experiment in which the participants were asked to discriminate between a virtual object and its physical counterpart. Our results indicate that the participants can only detect the discrepancy with a probability of 0.44. With such a level of perceptual realism, our display apparatus can facilitate a range of visual experiments that require the highest fidelity of reproduction while allowing for the full control of the displayed stimuli.

Limited GPU performance budgets and transmission bandwidths mean that real-time rendering often has to compromise on the spatial resolution or temporal resolution (refresh rate). A common practice is to keep either the resolution or the refresh rate constant and dynamically control the other variable. But this strategy is non-optimal when the velocity of displayed content varies. To find the best trade-off between the spatial resolution and refresh rate, we propose a perceptual visual model that predicts the quality of motion given an object velocity and predictability of motion. The model considers two motion artifacts to establish an overall quality score: non-smooth (juddery) motion, and blur. Blur is modeled as a combined effect of eye motion, finite refresh rate and display resolution. To fit the free parameters of the proposed visual model, we measured eye movement for predictable and unpredictable motion, and conducted psychophysical experiments to measure the quality of motion from 50 Hz to 165 Hz. We demonstrate the utility of the model with our on-the-fly motion-adaptive rendering algorithm that adjusts the refresh rate of a G-Sync-capable monitor based on a given rendering budget and observed object motion. Our psychophysical validation experiments demonstrate that the proposed algorithm performs better than constant-refresh-rate solutions, showing that motion-adaptive rendering is an attractive technique for driving variable-refresh-rate displays.

Structural or geometric classification of three-dimensional (3D) point clouds of urban regions from airborne LiDAR enables feature (object-based) classification, and 3D reconstruction. Here, we consider positive semidefinite symmetric second-order tensors as local geometric descriptors (LGDs), which gives structural classification. We compute LGDs using local neighborhood, and their eigenvalue-based features are conventionally used for object-based classification and 3D reconstruction. We combine derivative based 2D gradient energy tensor, and anisotropically diffused 3D voting tensor, using a multi-scale approach, to compute a LGD. We represent LGDs as second-order tensors, and compare the relevant eigenvalue-based features and saliency maps obtained from them. We visually compare the outcomes of our LGD with conventionally used covariance matrix.