Search Results (54)

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article

Background: The motor response to human visual stimuli is unique and differs from the reaction to light-based visual stimuli. While laboratory-based tests offer valuable insights into athletes’ basic perceptual–motor abilities, their translation to actual sports-specific tests is limited. Methods: Following a thorough warm-up, 44 collegiate-level male soccer players (age: 24.4 ± 2.5 y, mass: 63.01 ± 7.3 kg, stature: 167.62 ± 6.3 cm) from a tertiary institution completed the following tests: Sports Vision Test (20-light proactive speed test), 40 m sprint test (split times over 5, 10, 20 and 40 m), and a live Reactive Agility Test (RAT) entailing them to sprint, change direction either towards their dominant limb or non-dominant limb in response to a live tester, and sprint again. Results: Numerous moderate correlations were seen between the RAT and various sprint distances (r > 0.3, ES > 0.3, p < 0.05). The reaction speed relationship between the light-based (SVT) and live stimuli (RAT) test yielded a weak relationship (r > 0.4, ES > 0.5, p < 0.05). Furthermore, the light-based hand–eye coordination speed did not predict acceleration or top speed, while the total RAT time did explain 10.5% of top speed (40 m). No significant differences in the SVT average and total time were found among playing positions. Conclusions: The limited correlations observed indicate that light-based reaction training alone may not be sufficient to translate to field-based reactive agility; therefore, training should integrate perceptual–cognitive and motor demands. Future research should refine laboratory-based tests by incorporating contextual elements to enhance ecological validity and further investigate the transferability of these skills from controlled settings to real-world game scenarios. Full article

This study investigates L3 Spanish perception patterns among L1 Korean–L2 English bilinguals with varying L3 proficiency levels, aiming to test the applicability of traditional L2 perceptual models in multilingual contexts. We conducted two experiments: a cross-linguistic discrimination task and a cross-language identification task. Results revealed unexpected outcomes unique to multilingual contexts. Participants had difficulty reliably discriminating between cross-linguistic categories and showed little improvement over time. Similarly, they did not demonstrate progress in categorizing sounds specific to each language. The absence of a clear correlation between proficiency levels and the ability to discriminate and categorize sounds suggests that input distribution and language-specific activation may play more critical roles in L3 perception, consistent with the distributional learning approach. We argue that phoneme distributions from all three languages likely occupy a shared perceptual space. When a specific language is activated, the relevant phoneme distributions become dominant, while others are suppressed. This selective activation, while not crucial in traditional L1 and L2 studies, is critical in L3 contexts, like the one examined here, where managing multiple phonemic systems complicates discrimination and categorization. These findings underscore the need for theoretical adjustments in multilingual phonetic acquisition models and highlight the complexities of language processing in multilingual settings. Full article

Motorcycle horns are a dominant source of urban noise in many Southeast Asian cities, driven by high two-wheeler density and limited public transport infrastructure. Although automobiles have been in use for over a century, regulations governing horn design and volume control remain inadequate. This study investigates horn use behavior in Vietnamese urban traffic, identifying distinct acoustic patterns categorized as “attention” and “warning” signals. Measurements conducted in an anechoic chamber reveal that these patterns can increase sound pressure levels by up to 17 dB compared to standard horn use, with notable differences in frequency components. These levels often exceed the daytime noise thresholds recommended by the World Health Organization (WHO), indicating potential risks for adverse health outcomes, such as elevated stress, hearing damage, sleep disturbance, and cardiovascular effects. The findings are contextualized within broader efforts to manage traffic noise in rapidly developing urban areas. Drawing parallels with studies on aircraft noise exposure in Japan, this study suggests that long-term exposure, rather than peak noise levels alone, plays a critical role in shaping community sensitivity. The study results support the need for updated noise regulations that address both the acoustic and perceptual dimensions of road traffic noise. Full article

Dominant colors significantly influence visual image perception and are widely used in computer vision and design. Traditional extraction methods often neglect visually salient colors that occupy small areas yet possess high aesthetic relevance. This study introduces a method for detecting both dominant and visually prominent colors in a wide range of hues and images. We analyzed the color gamut of images in the CIE L^*a^*b^* color space and concluded that it is difficult to identify the dominant and prominent colors due to high color variability. To address these challenges, the proposed approach transforms images into the orthogonal ICaS color space, integrating the properties of RGB and CMYK models, followed by K-means clustering. A spectral residual saliency map is applied to exclude background regions and emphasize perceptually significant objects. Experimental evaluation on an image database shows that the proposed method yields color palettes with broader gamut coverage, preserved luminance, and visually balanced combinations. A comparative analysis was conducted using the ΔE₀₀ metric, which accounts not only for differences in lightness, chroma, and hue but also for the perceptual interactions between colors, based on their proximity in the color space. The results confirm that the proposed method exhibits greater color stability and aesthetic coherence than existing approaches. These findings highlight the effectiveness of the orthogonal saliency mean method for delivering a more perceptually accurate and visually consistent representation of the dominant colors in an image. This outcome validates the method’s applicability for image analysis and design. Full article

Vehicle detection algorithms constitute a fundamental pillar in intelligent driving systems and smart transportation infrastructure. Nevertheless, the inherent complexity and dynamic variability of traffic scenarios present substantial technical barriers to robust vehicle detection. While visual transformer-based detection architectures have demonstrated performance breakthroughs through enhanced perceptual capabilities, establishing themselves as the dominant paradigm in this domain, their practical implementation faces critical challenges due to the quadratic computational complexity inherent in the self-attention mechanism, which imposes prohibitive computational overhead. To address these limitations, this study introduces Mamba RT-DETR (MRD), an optimized architecture featuring three principal innovations: (1) We devise an efficient vehicle detection Mamba (EVDMamba) network that strategically integrates a linear-complexity state space model (SSM) to substantially mitigate computational overhead while preserving feature extraction efficacy. (2) To counteract the constrained receptive fields and suboptimal spatial localization associated with conventional SSM sequence modeling, we implement a multi-branch collaborative learning framework that synergistically optimizes channel dimension processing, thereby augmenting the model’s capacity to capture critical spatial dependencies. (3) Comprehensive evaluations on the BDD100K benchmark demonstrate that MRD architecture achieves a 3.1% enhancement in mean average precision (mAP) relative to state-of-the-art RT-DETR variants, while concurrently reducing parameter count by 55.7%—a dual optimization of accuracy and efficiency. Full article

While Jacques Rancière’s concept of the political, democracy, emancipation, equality and aesthetic have significantly (re)shaped many recent debates, his notion of dis-agreement—in its French formulation of ‘mésentente’, meaning the fact of not hearing, and/or of not understanding—has received relatively little attention. This article argues that if politics, as Rancière suggests, arise from a novel perceptual universe and if dis-agreement entails not-hearing and/or not-understanding, then “speaking politics”—the very act of breaking away from the dominant configuration of the police order—might be perceived as a noisy sound rather than as coherent and intelligible words. Drawing on Rancière’s concept of mésentente, this article examines the noisy, and largely unintelligible, protests sparked by the violent femicide of Giulia Cecchettin which occurred in Italy in 2023. Ultimately, it raises the following questions: which words are intelligible? Does intelligibility depend on the voice of the speaking subjects? Or does it hinge on the (un)familiarity of the vision they project? How can acts of politics be recognized if the words used are unintelligible? Full article

Incomplete communication networks (e.g., time delay and packet loss/switching) in haptic interaction and remote teleoperation systems can degrade both user performance and system stability. In this study, we hypothesized that human operator performance would decrease monotonically as network imperfections worsened. To test this hypothesis, we conducted two psychophysical experiments measuring the just-noticeable difference (JND), point of subjective equality (PSE), and perception time under varying conditions of packet separation time and packet loss. Our findings show that increasing packet separation time significantly elevated both JND and PSE, indicating a poorer discrimination ability and a systematic bias toward perceiving the environment as stiffer. By contrast, packet loss rates of up to 75% had no significant impact on perceptual performance, suggesting that, at sufficiently high sampling rates, human operators can compensate for substantial data loss. Overall, the results underscore that packet separation time, rather than packet loss, is the dominant factor affecting perceptual performance in haptic teleoperation. Full article

In the era of deepfakes and AI-generated content, digital image manipulation poses significant challenges to image authenticity, creating doubts about the credibility of images. Traditional image forensics techniques often struggle to detect sophisticated tampering, and passive detection approaches are reactive, verifying authenticity only after counterfeiting occurs. In this paper, we propose a novel full-resolution secure learned image codec (SLIC) designed to proactively prevent image manipulation by creating self-destructive artifacts upon re-compression. Once a sensitive image is encoded using SLIC, any subsequent re-compression or editing attempts will result in visually severe distortions, making the image’s tampering immediately evident. Because the content of an SLIC image is either original or visually damaged after tampering, images encoded with this secure codec hold greater credibility. SLIC leverages adversarial training to fine-tune a learned image codec that introduces out-of-distribution perturbations, ensuring that the first compressed image retains high quality while subsequent re-compressions degrade drastically. We analyze and compare the adversarial effects of various perceptual quality metrics combined with different learned codecs. Our experiments demonstrate that SLIC holds significant promise as a proactive defense strategy against image manipulation, offering a new approach to enhancing image credibility and authenticity in a media landscape increasingly dominated by AI-driven forgeries. Full article

In estimating the global carbon cycle, the net ecosystem exchange (NEE) is crucial. The understanding of the mechanism of interaction between NEE and various environmental factors of ecosystems has been very limited, and the interactions between the factors are intricate and complex, which leads to difficulties in accurately estimating NEE. In this study, we propose the A-DMLP (attention-deep multilayer perceptron)-deep learning model for NEE simulation as well as an interpretability study using the SHapley Additive exPlanations (SHAP) model. The attention mechanism was introduced into the deep multilayer perceptual machine, and the important information in the original input data was extracted using the attention mechanism. Good results were obtained on nine eddy covariance sites in China. The model was also compared with the random forest, long short-term memory, deep neural network, and convolutional neural networks (1D) models to distinguish it from previous shallow machine learning models to estimate NEE, and the results show that deep learning models have great potential in NEE modeling. The SHAP method was used to investigate the relationship between the input features of the A-DMLP model and the simulated NEE, and to enhance the interpretability of the model. The results show that the normalized difference vegetation index, the enhanced vegetation index, and the leaf area index play a dominant role at most sites. This study provides new ideas and methods for analyzing the intricate relationship between NEE and environmental factors by introducing the SHAP interpretable model. These advancements are crucial in achieving carbon reduction targets. Full article

Food evaluation is a topic central to consumer research and food marketing. However, there is little consensus regarding how consumers combine sensory stimuli, product information, and visual impressions to shape their evaluation. Moreover, the bulk of research relies on studies based on questionnaires and declarative responses, raising questions about subliminal processes and their hierarchy in an evaluation process. To address this gap in the literature, we conducted a study with more than 400 participants in Morocco and Tunisia and investigated how factors such as flavor/taste, product information, and packaging design in a variety of olive oils influence visual attention and are reflected in willingness to pay (WTP). We implemented incentivization through an auction to reduce the hypothetical bias in stated WTP values. The results revealed that, compared to tasting the oils, the provision of cognitive information led to an increase in consumers’ WTP. However, a drastic increase in WTP occurred when the consumers were exposed to package designs, overshadowing the formerly dominant effects of product attributes. These findings support theories suggesting a visual perceptual processing advantage due to the picture superiority effect–a picture says a thousand words. Further, it underlines the importance of graphic design in food marketing. The findings have ramifications for food marketing, product development, and pricing strategies. Full article

A lightscape, as a special visual landscape, has unique temporal and spatial characteristics that traditional photometric measurements and descriptions struggle to capture accurately. Despite their significance, there is a lack of in-depth understanding of the on-site perception of lightscapes’ temporal and spatial characteristics, including in outdoor university campus spaces. This study aims to explore the temporal and spatial characteristics of outdoor lightscapes on university campuses and their potential perceptual influencing factors, providing sustainable design, planning, and management suggestions for campus lightscapes. This study was conducted in the Wushan Campus of the South China University of Technology. It employs a “lightwalk” method for on-site perception evaluation, combining qualitative and quantitative approaches to investigate the temporal and spatial characteristics of lightscapes in outdoor university campus spaces and the effect of temporal and spatial factors on lightscape perception. The main findings are as follows: (1) Meteorological, architectural, and biophilic lightscape dominate the outdoor campus lightscapes. (2) The temporal and spatial characteristics of the lightscapes are affected by changes in natural light, the transition of light sources from day to night, human activity patterns, and the functional characteristics of the site. (3) The correlation between meteorological and traffic lightscape types and lightscape perception is diminished during the evening periods. This method should be a good way to optimize water and architectural lightscape at night to alleviate discomfort. (4) In green-shaded spaces, the association between meteorological, architectural, and traffic lightscape types and the evaluations of emotional, luminous, and eventful aspects is significantly enhanced, suggesting an increase in such spaces to improve lightscape experience quality. This study advocates that the construction of buildings and environments should be “human-oriented”, paying attention to the scientific foundation that humans perceive the habitat environment through the “five senses”. Research on lightscapes’ temporal and spatial characteristics, exploration of the temporal and spatial modes of lightscape perception, and avoiding energy waste and light pollution are conducive to the design and construction of university campuses in line with the principles of sustainable development. The lightscape optimization strategies derived from this study can not only provide practical guidance for the design and management of campus but also may offer valuable recommendations for planning sustainable campus development. Full article

The aim of the present study was to evaluate skin temperature (Tsk) asymmetries, using infrared thermography, in professional padel players before (PRE), after (POST) and 10 min after training (POST10), and their relationship with perceptual variables and training characteristics. Thermal images were taken of 10 players before, after and 10 min after a standardized technical training. After training, Tsk of the dominant side was higher than before training in the anterior forearm (30.8 ± 0.4 °C vs. 29.1 ± 1.2 °C, p < 0.01; ES = 1.9), anterior shoulder (31.6 ± 0.6 °C vs. 30.9 ± 0.6 °C, p < 0.05; ES = 1.0) posterior arm (29.5 ± 1.0 °C vs. 28.3 ± 1.2 °C, p < 0.05; ES = 1.0), and posterior forearm (30.8 ± 0.9 °C vs. 29.3 ± 1.6 °C, p < 0.05; ES = 1.1). Likewise, these differences were significant POST10 in the anterior arm, anterior forearm, anterior shoulder, posterior arm and posterior forearm. Comparing the different moments of measurement (PRE, POST and POST10), the temperature was higher POST10 in all the regions analyzed except for the shoulder, abdominals, and lower back. Also, correlations were found between fatigue variation and temperature variation between limbs (Tsk dominance), and no correlation was found except between age and posterior thigh (|r| = 0.69; p < 0.05), and between the racket mass and anterior knee (|r| = 0.81; p < 0.01). In conclusion, infrared thermography allows monitoring of skin asymmetries between limbs in professional padel players, but these asymmetries were not related to overall fatigue variation, overall pain variation, years of experience and training hours. Full article

Artificial intelligence (AI) has become popular worldwide after technological breakthroughs in the early 2010s. Accordingly, many organizations and individuals have been using AI for various applications. Previous research has been dominated by case studies regarding the industrial use of AI, although how time-series changes affect users’ perceptions has not been clarified yet. This study analyzes time-series changes in AI perceptions through text mining from nonfinancial information obtained from Japanese firms’ disclosures. The main findings of this study are as follows: first, perceptions of AI vary across industries; second, the business sector has progressed through the stages of recognition, investment, strategization, commercialization, and monetization. This transition is concurrent with each category’s evolving interpretation of the innovator theory proposed by Rogers (2003), to some extent. Third, it took approximately a decade from the breakthrough technology to the monetization by Japanese firms. Our findings underline the importance of speeding up the organizational process through intervention and contribution to the areas regarding “diffusion of innovation” and perceptual characteristics. Full article

Faces and bodies both provide cues to age and cuteness, but little work has explored their interaction in cuteness perception. This study examines the interplay of facial and bodily cues in the perception of cuteness, particularly when these cues convey conflicting age information. Participants rated the cuteness of face–body composites that combined either a child or adult face with an age-congruent or incongruent body alongside manipulations of the head-to-body height ratio (HBR). The findings from two experiments indicated that child-like facial features enhanced the perceived cuteness of adult bodies, while child-like bodily features generally had negative impacts. Furthermore, the results showed that an increased head size significantly boosted the perceived cuteness for child faces more than for adult faces. Lastly, the influence of the HBR was more pronounced when the outline of a body’s silhouette was the only available information compared to when detailed facial and bodily features were presented. This study suggests that body proportion information, derived from the body’s outline, and facial and bodily features, derived from the interior surface, are integrated to form a unitary representation of a whole person in cuteness perception. Our findings highlight the dominance of facial features over bodily information in cuteness perception, with facial attributes serving as key references for evaluating face–body relationships and body proportions. This research offers significant insights into social cognition and character design, particularly in how people perceive entities with mixed features of different social categories, underlining the importance of congruency in perceptual elements. Full article