In collaborative and distributed intelligent systems, compressed intermediate features are routinely transmitted and reused, making semantic quality assessment (SQA) crucial for reliable deployment. Recent compressed feature quality assessment (CFQA) benchmarks, however, show that conventional similarity measures often correlate poorly with downstream semantic utility and lack robustness across diverse feature codecs. In this paper, we propose a token-wise, attention-guided method for assessing the semantic quality of compressed features. First, motivated by the observation that many downstream heads normalize and process tokens largely independently, we assess quality at the token level. This token-wise formulation exploits the intrinsic correspondence between the original and reconstructed tokens while reducing cross-token interference. Second, since tokens contribute unequally to downstream task performance, we adopt an attention-guided aggregation scheme: we derive task-adaptive importance weights from DINOv2 self-attention and use them to pool token-wise quality predictions into a global semantic quality score. Third, to accommodate heterogeneous supervision across tasks, we cast CFQA as a regression problem and rescale classification-based rank targets to mitigate label imbalance. Experiments on the CFQA benchmark demonstrate that our method consistently improves PLCC and SROCC across three tasks and four codecs, yielding a practical, codec-agnostic quality interface for next-generation intelligent systems.
Current Image Quality Assessment (IQA) models are often constrained by the inconsistency of Mean Opinion Score (MOS) scales across different datasets. While traditional regression-based approaches (MSE) require complex MOS alignment, traditional ranking losses often ignore the magnitude of quality differences between stimuli. In this paper, we propose a Weighted Pairwise Hinge Loss (WPHL) that enables seamless training across multiple datasets without manual score calibration. By incorporating a dynamic margin proportional to the ground-truth quality gap and an importance-weighting mechanism, our loss function captures the nuanced topology of image quality perception. We demonstrate that WPHL allows lightweight architectures to rival significantly larger ones. Our approach offers a scalable path toward a “foundation model” for IQA by unlocking the use of diverse, multi-source training data. In the future, WPHL might be used to increase the performance of existing state-of-the-art models or help in the introduction of new ones.
Evaluating 8K ultra-high-definition (UHD) video quality is challenging, as viewer expertise strongly influences visual behavior. This study investigates the strategic differences between experts and non-experts during single-stimulus 8K video quality assessment (VQA). First, analysis using Coefficient K, a metric to distinguish ambient/focal attention, shows that experts adopt a focal strategy to scrutinize localized artifacts, whereas non-experts maintain an ambient pattern. Second, spatial analysis through bivariate contour ellipse area (BCEA), an indicator of spatial gaze concentration, and gaze density heatmaps show that experts' gaze is more localized and targets artifact-prone textures from the initial phase, while non-experts tend to be influenced by center bias. Finally, we demonstrate that gaze velocity, integrated with a gaze-based local quality metric, significantly predicts subjective scores. While this combination improves overall performance, variations across content types highlight the potential of temporal gaze dynamics to further refine quality models. Our findings advocate for a dual-axis framework in 8K VQA that incorporates conventional spatial gaze distribution (“Where”) with expertise-driven temporal gaze dynamics (“How”).
This paper investigates the impact of interactioninduced body movement on cybersickness in Virtual Reality (VR). Therefore we designed a controlled VR environment with three interaction conditions of increasing movement complexity. Data from 24 participants, combining subjective measures and motionderived features, show a consistent increase in cybersickness with higher movement demands, particularly in conditions involving vertical displacement and full-body interaction. Moreover, correlation analysis reveals a significant negative relationship between the coefficient of variation of head velocity and cybersickness. This suggests that more variable and adaptive motion may mitigate the discomfort. These findings highlight the importance of movement characteristics, beyond movement intensity alone, for designing full-body interactive VR experiences.
Visual artefacts in 3D meshes can act as local distractors and perturb visual attention, which may affect how users inspect and interpret interactive 3D content in applications such as technology-enhanced learning and training. An important open question is how localized geometric distortions translate into measurable perceptual disruption during interactive viewing, as understanding this relationship is necessary for developing perceptually grounded evaluation methods for 3D assets. In this work, we investigate whether gaze can serve as an implicit, spatially localized indicator of such effects. To this end, we introduce a surface-based gaze analysis approach that maps eye-tracking samples onto the visible 3D mesh geometry under user-driven interaction, enabling view-consistent analysis of localized attention. This enables defect-level, interaction-aware attention analysis that is not supported by conventional image-plane or fixed-viewpoint methods. In a study with 22 participants viewing 27 textureless meshes with four defect types, we observe that self-intersection defects produce significantly higher fixation counts, while time-to-first fixation (TFF) does not show significant differences compared to corresponding regions in unmodified meshes. In contrast, smoothing defects shows weaker trends, while low-polygon and semantic defects do not produce reliable localized effects under the present conditions. These results suggest that visual attention reflects the perceptual severity of localized mesh distortions, positioning gaze as a complementary signal for perceptually grounded quality-of-experience assessment in 3D content. The findings are conditioned by the controlled viewing setup and defect placement, and should be interpreted accordingly.
