Image and video quality metrics, such as SSIM, LPIPS, and VMAF, aim to predict perceived visual quality and are often assumed to reflect principles of human vision. However, relatively few metrics explicitly incorporate models of human perception, with most relying on hand-crafted formulas or data-driven training to approximate perceptual alignment. In this paper, we introduce a set of tests for full-reference quality metrics that evaluate their ability to capture key aspects of low-level human vision: contrast sensitivity, contrast masking, and contrast matching. These tests provide an additional framework for assessing both established and newly proposed metrics. We apply the tests to 34 existing quality metrics and highlight patterns in their behavior, including the ability of LPIPS and MS-SSIM to predict contrast masking and the tendency of SSIM to overemphasize high spatial frequencies, which is mitigated in MS-SSIM, and the general inability of metrics to model supra-threshold contrast constancy. Our results demonstrate how these tests can reveal properties of quality metrics that are not easily observed with standard evaluation protocols.

Computer vision foundation models, such as DINO or OpenCLIP, are trained in a self-supervised manner on large image datasets. Analogously, substantial evidence suggests that the human visual system (HVS) is influenced by the statistical distribution of colors and patterns in the natural world, characteristics also present in the training data of foundation models. The question we address in this paper is whether foundation models trained on natural images mimic some of the low-level characteristics of the human visual system, such as contrast detection, contrast masking, and contrast constancy. Specifically, we designed a protocol comprising nine test types to evaluate the image encoders of 45 foundation and generative models. Our results indicate that some foundation models (e.g., DINO, DINOv2, and OpenCLIP), share some of the characteristics of human vision, but other models show little resemblance. Foundation models tend to show smaller sensitivity to low contrast and rather irregular responses to contrast across frequencies. The foundation models show the best agreement with human data in terms of contrast masking. Our findings suggest that human vision and computer vision may take both similar and different paths when learning to interpret images of the real world. Overall, while differences remain, foundation models trained on vision tasks start to align with low-level human vision, with DINOv2 showing the closest resemblance. Our code is available on https://github.com/caiyancheng/VFM_HVS_CVPR2025.

While it is well recognized that the visibility of distortions is affected by the viewing distance and display peak luminance, very few datasets control those conditions, and also few video quality metrics can account for them. To address this gap, we collected a new video quality dataset, HDR-VDC, which captures the quality degradation of HDR content due to AV1 coding artifacts and the resolution reduction. The quality drop was measured at two viewing distances, corresponding to 60 and 120 pixels per visual degree, and two display mean luminance levels, 51 and 5.6 nits. In contrast to the existing datasets that use direct rating protocol, we employ a highly sensitive pairwise comparison protocol with active sampling and comparisons across viewing distances to ensure possibly accurate quality measurements. We also provide the first publicly available dataset that measures the effect of display peak luminance and includes HDR videos encoded with AV1. Our results indicate that the effect of both viewing distance and display luminance is significant, and it reduces the visibility of coding and upsampling artifacts on dimmer displays or those seen from a further distance. The dataset is available at https://doi.org/10.17863/CAM.107964 and the code at https://github.com/gfxdisp/HDR-VDC.

Viewing distance and display resolution have ar-guably a significant impact on perceived image quality; images seen on a mobile phone with high pixel density reveal fewer distortions than the same images seen on a large TV from a close distance. However, only a few image and video quality metrics account for the effect of viewing distance and resolution. Those that do, typically rely on contrast sensitivity functions (CSFs) of the visual system. Other metrics can be potentially adapted to different viewing distances by rescaling input images. In this paper, we investigate the performance of such adapted metrics together with those that natively account for viewing distance. The results for three testing datasets indicate that there is no evidence that the metrics based on the CSF outperform those that rely on rescaled images. Moreover, we found that both methods are not successful to account for the changes in quality introduced by the change in viewing distance. We conclude that accounting for viewing distances requires better models.