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Multi-Exposure Fusion (MEF) technique aims to fuse multiple images taken from the same scene at different exposure levels into an image with more details. Although more and more MEF algorithms have been developed, how to effectively evaluate the quality of MEF images has not been thoroughly investigated. To address this issue, a blind quality evaluator for MEF image via joint sparse features and complex-wavelet statistical characteristics is developed. Specifically, considering that color and structure distortions are inevitably introduced during the MEF operations, we first train a color dictionary in the Lab color space based on the color perception mechanism of human visual system, and extract sparse perceptual features to capture the color and structure distortions. Given an MEF image to be evaluated, its components in both luminance and color channels are derived first. Subsequently, these obtained components are sparsely encoded using the trained color dictionary, and the perceived sparse features are extracted from the derived sparse coefficients. In addition, considering the insensitivity of sparse features towards weak structural information in images, complex steerable pyramid decomposition is further performed over the generated chromaticity map. Consequently, perceptual features of magnitude, phase and cross-scale structural similarity index are extracted from complex wavelet coefficients within the chromaticity map as quality-aware features. Experimental results demonstrate that our proposed metric outperforms the existing classic image quality evaluation metrics while maintaining high accordance with human visual perception. [ABSTRACT FROM AUTHOR]

This paper conducts a scientific assessment of aesthetic quality to provide intuitive and scientific planning strategies for national park construction. Focusing on Qianjiangyuan National Park, the study used the scenic beauty evaluation (SBE) method to subjectively assess landscape photos from 16 sample sites. Objective eye movement indicators describing visual behavior were also analyzed. A national park landscape visual quality assessment model was derived through multiple linear regressions correlating subjective evaluations with objective indicators. Spatial technologies like ArcGIS were used to analyze the visual quality and its spatial distribution. Key findings include (1) subjective evaluations showed higher SBE scores for wetland landscapes, followed by recreational, village, and forest landscapes, (2) eye movement behavior varied across landscape types, with the forest landscape having the shortest first fixation time and the lowest saccade frequency, while recreational landscapes had the lowest average saccade speed, (3) strong correlations were found between SBE and indicators such as average fixation time and saccade frequency, with fixation duration ratio being the leading factor influencing visual aesthetic quality, and (4) visual aesthetic quality was highest in the north and south areas of the park, with significant differences between sample sites in these regions compared to the central area. Among different functional zones, the ecological protection area had the highest quality, while the Suzhuang management area excelled in aesthetic quality compared to the Hetian management area. [ABSTRACT FROM AUTHOR]

Abstract We present a study on the validity of quality assessment in the context of the development of visual media coding schemes. The work is motivated by the need for reliable means for decision-taking in standardization efforts of MPEG and JVET, i.e., the adoption or rejection of coding tools during the development process of the coding standard. The study includes results considering three means: objective quality metrics, remote expert viewing, which is a method designed in the context of MPEG standardization, and formal laboratory visual evaluation. The focus of this work is on the comparison of pairs of coded video sequences, e.g., a proposed change and an anchor scheme at a given rate point. An aggregation of performance measurements across multiple rate points, such as the Bjøntegaard Delta rate, is out of the scope of this paper. The paper details the test setup for the subjective assessment methods and the objective quality metrics under consideration. The results of the three approaches are reviewed, analyzed, and compared with respect to their suitability for the decision-taking task. The study indicates that, subject to the chosen test content and test protocols, the results of remote expert viewing using a forced-choice scale can be considered more discriminatory than the results of naïve viewers in the laboratory tests. The results further that, in general, the well-established quality metrics, such as PSNR, SSIM, or MS-SSIM, exhibit a high rate of correct decision-making when their results are compared with both types of viewing tests. Among the learning-based metrics, VMAF and AVQT appear to be most robust. For the development process of a coding standard, the selection of the most suitable means must be guided by the context, where a small number of carefully selected objective metrics, in combination with viewing tests for unclear cases, appears recommendable.

We present a study on the validity of quality assessment in the context of the development of visual media coding schemes. The work is motivated by the need for reliable means for decision-taking in standardization efforts of MPEG and JVET, i.e., the adoption or rejection of coding tools during the development process of the coding standard. The study includes results considering three means: objective quality metrics, remote expert viewing, which is a method designed in the context of MPEG standardization, and formal laboratory visual evaluation. The focus of this work is on the comparison of pairs of coded video sequences, e.g., a proposed change and an anchor scheme at a given rate point. An aggregation of performance measurements across multiple rate points, such as the Bjøntegaard Delta rate, is out of the scope of this paper. The paper details the test setup for the subjective assessment methods and the objective quality metrics under consideration. The results of the three approaches are reviewed, analyzed, and compared with respect to their suitability for the decision-taking task. The study indicates that, subject to the chosen test content and test protocols, the results of remote expert viewing using a forced-choice scale can be considered more discriminatory than the results of naïve viewers in the laboratory tests. The results further that, in general, the well-established quality metrics, such as PSNR, SSIM, or MS-SSIM, exhibit a high rate of correct decision-making when their results are compared with both types of viewing tests. Among the learning-based metrics, VMAF and AVQT appear to be most robust. For the development process of a coding standard, the selection of the most suitable means must be guided by the context, where a small number of carefully selected objective metrics, in combination with viewing tests for unclear cases, appears recommendable. [ABSTRACT FROM AUTHOR]

Abstract Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods for determining the visual quality of these assets. While visual quality assessment has been an active area of research for a number of years, few publications consider this process in the context of asset creation in digital twins. In this work, we introduce an automated decimation procedure using machine learning to assess the visual impact of decimation, a process commonly used in the production of 3D assets which has thus far been underrepresented in the visual assessment literature. Our model combines 108 geometric and perceptual metrics to determine if a 3D object has been unacceptably distorted during decimation. Our model is trained on almost 4, 000 distorted meshes, giving a significantly wider range of applicability than many models in the literature. Our results show a precision of over 97% against a set of test models, and performance tests show our model is capable of performing assessments within 2 minutes on models of up to 25, 000 polygons. Based on these results we believe our model presents both a significant advance in the field of visual quality assessment and an important step towards reducing the cost of virtual and augmented reality-based digital-twins.

Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods for determining the visual quality of these assets. While visual quality assessment has been an active area of research for a number of years, few publications consider this process in the context of asset creation in digital twins. In this work, we introduce an automated decimation procedure using machine learning to assess the visual impact of decimation, a process commonly used in the production of 3D assets which has thus far been underrepresented in the visual assessment literature. Our model combines 108 geometric and perceptual metrics to determine if a 3D object has been unacceptably distorted during decimation. Our model is trained on almost 4, 000 distorted meshes, giving a significantly wider range of applicability than many models in the literature. Our results show a precision of over 97% against a set of test models, and performance tests show our model is capable of performing assessments within 2 minutes on models of up to 25, 000 polygons. Based on these results we believe our model presents both a significant advance in the field of visual quality assessment and an important step towards reducing the cost of virtual and augmented reality-based digital-twins. [ABSTRACT FROM AUTHOR]

This paper conducts a scientific assessment of aesthetic quality to provide intuitive and scientific planning strategies for national park construction. Focusing on Qianjiangyuan National Park, the study used the scenic beauty evaluation (SBE) method to subjectively assess landscape photos from 16 sample sites. Objective eye movement indicators describing visual behavior were also analyzed. A national park landscape visual quality assessment model was derived through multiple linear regressions correlating subjective evaluations with objective indicators. Spatial technologies like ArcGIS were used to analyze the visual quality and its spatial distribution. Key findings include (1) subjective evaluations showed higher SBE scores for wetland landscapes, followed by recreational, village, and forest landscapes, (2) eye movement behavior varied across landscape types, with the forest landscape having the shortest first fixation time and the lowest saccade frequency, while recreational landscapes had the lowest average saccade speed, (3) strong correlations were found between SBE and indicators such as average fixation time and saccade frequency, with fixation duration ratio being the leading factor influencing visual aesthetic quality, and (4) visual aesthetic quality was highest in the north and south areas of the park, with significant differences between sample sites in these regions compared to the central area. Among different functional zones, the ecological protection area had the highest quality, while the Suzhuang management area excelled in aesthetic quality compared to the Hetian management area.

In the fields of image processing and computer vision, evaluating blind image quality (BIQA) is still a difficult task. In this paper, a unique BIQA framework is presented that integrates feature extraction, feature selection, and regression using a support vector machine (SVM). Various image characteristics are included in the framework, such as wavelet transform, prewitt and gaussian, log and gaussian, and prewitt, sobel, and gaussian. An SVM regression model is trained using these features to predict the quality ratings of photographs. The proposed model uses the Information Gain attribute approach for feature selection to improve the performance of the regression model and decrease the size of the feature space. Three commonly used benchmark datasets, TID2013, CSIQ, and LIVE, are utilized to assess the performance of the proposed methodology. The study examines how various feature types and feature selection strategies affect the functionality of the framework through thorough experiments. The experimental findings demonstrate that our suggested framework reaches the highest levels of accuracy and robustness. This suggests that it has a lot of potential to improve the accuracy and dependability of BIQA approaches. Additionally, its use is broadened to include image transmission, compression, and restoration. Overall, the results demonstrate our framework's promise and ability to advance studies into image quality assessment. [ABSTRACT FROM AUTHOR]

Over the past decade, three-dimensional (3D) graphics have become highly detailed to mimic the real world, exploding their size and complexity. Certain applications and device constraints necessitate their simplification and/or lossy compression, which can degrade their visual quality. Thus, to ensure the best Quality of Experience, it is important to evaluate the visual quality to accurately drive the compression and find the right compromise between visual quality and data size. In this work, we focus on subjective and objective quality assessment of textured 3D meshes. We first establish a large-scale dataset, which includes 55 source models quantitatively characterized in terms of geometric, color, and semantic complexity, and corrupted by combinations of five types of compression-based distortions applied on the geometry, texture mapping, and texture image of the meshes. This dataset contains over 343k distorted stimuli. We propose an approach to select a challenging subset of 3,000 stimuli for which we collected 148,929 quality judgments from over 4,500 participants in a large-scale crowdsourced subjective experiment. Leveraging our subject-rated dataset, a learning-based quality metric for 3D graphics was proposed. Our metric demonstrates state-of-the-art results on our dataset of textured meshes and on a dataset of distorted meshes with vertex colors. Finally, we present an application of our metric and dataset to explore the influence of distortion interactions and content characteristics on the perceived quality of compressed textured meshes. [ABSTRACT FROM AUTHOR]

Equipping unmanned aerial systems (UASs) with light detection and ranging (lidar) has been made possible with recent advancements, which has made these sensors compact and gradually more cost-effective. Despite the increased proliferation of UAS-lidar in several fields, the geometric accuracy of lidar-generated point clouds, together with their visual qualities, needs to be explored for building surveying applications. Considering that red−blue−green (RGB) cameras are the most prevalent UAS sensors in building surveying, a lidar- and an RGB camera-equipped UAS was deployed on a mixed infrastructure to simultaneously collect data and generate corresponding point clouds. Different geometric features from both RGB and lidar point clouds were measured and compared quantitatively against benchmark field observations. A qualitative analysis on the point clouds' visual qualities was also performed and a sensor recommendation matrix was proposed based on desired application accuracy. Lidar has proven to be a viable alternative, providing better geometric accuracy, data quality, and clarity in all three dimensions. [ABSTRACT FROM AUTHOR]

Holographic displays have the promise to be the ultimate 3D display technology, able to account for all visual cues. Recent advances in photonics and electronics gave rise to high-resolution holographic display prototypes, indicating that they may become widely available in the near future. One major challenge in driving those display systems is computational: computer generated holography (CGH) consists of numerically simulating diffraction, which is very computationally intensive. Our goal in this paper is to give a broad overview of the state-of-the-art in CGH. We make a classification of modern CGH algorithms, we describe different algorithmic CGH acceleration techniques, discuss the latest dedicated hardware solutions and indicate how to evaluate the perceptual quality of CGH. We summarize our findings, discuss remaining challenges and make projections on the future of CGH.

The Yangshuo Section of the Lijiang River Basin (YS of LRB) is the essence of the Guilin landscape in China. The typical karst landforms and changing topography form a unique forest landscape in YS of LRB. In order to reveal the visual quality of itse forest landscape, this research used GIS spatial analysis technology combined with Analytic Hierarchy Process (AHP), the assessment factors were leveled and assigned, and some abstract assessment indicators in the visual quality assessment were quantified. Three primary indicators of forest landscape visual quality (FLVQ) assessment in YS of LRB, including Scenic Quality (SQ), Visual Sensitivity (VS), and Visual Absorption Capability (VAC), are proposed. Visual assessment units are divided based on the watershed, and a process and framework for the comprehensive assessment of FLVQ was established at the watershed scale. The results show that the FLVQ in YS of LRB is generally at a low level. The area percentage of FLVQ at high (13′&11′), medium (9′) and low (7′&5′) levels are 10.95%, 29.67% and 59.38%, respectively. The comprehensive score of FLVQ in the karst area is slightly lower than that in the non-karst area, but the karst area of the units with the highest FLVQ accounts for 99.58% of the FLVQ of karst areas along the Lijiang River distributed in the Lijiang River Scenic Area, greater than that of any other areas. The FLVQ in regions with high vegetation coverage and large topographic changes is relatively higher. Slope, relief amplitude, and vegetation richness directly affect the visual quality of forest landscape. Under the influence of local scenic area protection policies and relatively stable natural climate, the FLVQ in YS of LRB has maintained a stable level in the past 20 years. The areas with low FLVQ are mainly cultivated land and construction land, which have low vegetation coverage and no obvious change in terrain. Based on the comprehensive assessment results of FLVQ in YS of LRB, the management goals and future development suitability of forest landscape is discussed. This research proposes appropriate construction and management strategies for forest landscape structures. The results are helpful for providing a scientific research basis for forest landscape resource conservation, landscape site selection, and forest ecotourism development in the Karst landscape area. The impact of human disturbance on the forest landscape fragmentation and the growth characteristics of forests under different landforms are the focus of future FLVQ research on similar areas. [ABSTRACT FROM AUTHOR]

Multi-view 3D content is subject to distortions during the process of depth image-based rendering (DIBR). Studies have shown the unreliable performance of the well-established image quality assessment (IQA) models for evaluation of DIBR-synthesized views which surge the need for more effective IQA methods. Existing objective methods generally rely on the pixel-wise correspondences between the reference and distorted images, while view synthesis can introduce pixel shifts. DIBR distortions such as stretching and local hole-filling errors have different visual impacts from conventional distortions, challenging the existing IQA models. Here, we developed a Full-Reference (FR) objective IQA metric for synthesized views that significantly outperforms 2D IQA and the state-of-the-art DIBR IQA approaches. While the pixel misalignment between the reference and synthesized views is a big challenge for quality assessment, we deployed a Convolutional Neural Network (CNN) model to acquire a feature representation that inherently offers resilience to the imperceptible pixel shift between the compared images. Therefore, our model does not need accurate shift compensation. We deployed a set of quality-aware CNN features representing high-order statistics, to measure the structural similarity which is combined with a semantic similarity measure for accurate quality assessment. Moreover, prediction accuracy is improved by incorporating a visual saliency model acquired using the activations of the higher CNN layers. Experimental results indicate a significant performance gain (14.6% in terms of Spearman’s rank-order correlation) compared to the top existing IQA model. The source code of the proposed metric is available at: https://gitlab.com/saeedmp/sequss.

Quality assessment is an integral stage in the processing and analysis of digital images in various automated systems. With the increase in the number and variety of devices that allow receiving data in various digital formats, as well as the expansion of human activities in which information technology (IT) is used, the need to assess the quality of the data obtained is growing. As well as the bar grows for the requirements for their quality.The article describes the factors that deteriorate the quality of digital images, areas of application of image quality assessment functions, a method for normalizing proximity measures, classes of digital images and their possible distortions, image databases available on the Internet for conducting experiments on assessing image quality with visual assessments of experts.

Forest-based destinations rely on quality forest landscapes as a critical resource, and recently the use of psychophysiological techniques that monitor the electrophysiological responses of the brain has gained research attention in forest landscape assessment. This study innovatively employed a mobile EEG methodology to investigate 130 participants' attentional changes as they walked through the tourist tracks of a national park, and adopted a multi-method design triangulating the findings of a psychometric experiment, self-report measures, and expert assessments of forest landscapes. Results demonstrated a strong correlation between visitors' attentional changes over time and on different tourist tracks, based on test–retest and alternate-form reliability testing. Moreover, the brain attention values showed some correspondence with the perceived visual quality of forest landscapes as rated by experts, and the consistency between the EEG and expert approaches was particularly evident in relation to landscapes of high and low aesthetic quality. Results provided persuasive evidence for the use of mobile EEG devices as a supplementary or alternative measure of visual quality assessment of forest landscape, and to inform landscape planning and experience design in forest-based destinations. [ABSTRACT FROM AUTHOR]

In the fields of image processing and computer vision, evaluating blind image quality (BIQA) is still a difficult task. In this paper, a unique BIQA framework is presented that integrates feature extraction, feature selection, and regression using a support vector machine (SVM). Various image characteristics are included in the framework, such as wavelet transform, prewitt and gaussian, log and gaussian, and prewitt, sobel, and gaussian. An SVM regression model is trained using these features to predict the quality ratings of photographs. The proposed model uses the Information Gain attribute approach for feature selection to improve the performance of the regression model and decrease the size of the feature space. Three commonly used benchmark datasets, TID2013, CSIQ, and LIVE, are utilized to assess the performance of the proposed methodology. The study examines how various feature types and feature selection strategies affect the functionality of the framework through thorough experiments. The experimental findings demonstrate that our suggested framework reaches the highest levels of accuracy and robustness. This suggests that it has a lot of potential to improve the accuracy and dependability of BIQA approaches. Additionally, its use is broadened to include image transmission, compression, and restoration. Overall, the results demonstrate our framework’s promise and ability to advance studies into image quality assessment.

The luminance dynamic range in natural scenes is exceedingly wide. However, capturing the wide dynamic range information of natural scenes through a single exposure is challenging for commercial light field cameras. One solution to expand the luminance dynamic range of captured light field images is to employ multi-exposure imaging techniques. Nevertheless, this approach may introduce various distortions during the image fusion procedure, especially for dynamic scenes. To evaluate the visual quality of multi-exposure fused light field images (MEFLFIs) and compare the performance of different multi-exposure light field fusion methods, we initially conduct a subjective quality assessment on MEFLFIs for dynamic scenes and establish the first MEFLFI benchmark dataset, which comprises 480 fused light field images generated by eleven state-of-the-art multi-exposure fusion algorithms and two tone-mapping algorithms, along with their corresponding subjective scores. Subsequently, a novel specially designed objective quality assessment metric for MEFLFIs is developed, which incorporates both local–global joint features and angular quality-aware features. The key contribution of this work lies in establishing a pioneering benchmark dataset for MEFLFIs along with a novel tailored objective quality metric. Experimental results conducted over our established MEFLFI database demonstrate that the proposed metric achieves superior performance, with Pearson linear correlation coefficient (PLCC) and Spearman rank correlation coefficient (SROCC) values of 0.8895 and 0.8658 respectively, representing a significant improvement of approximately 5.7 % and 4.9 % compared to the second-best specifically-designed LFI quality metric available. Furthermore, ablation experiments have confirmed that the integration of local–global joint features and angular features significantly enhances the performance of our objective metric, resulting in a better alignment with human visual perception. [ABSTRACT FROM AUTHOR]

For 360° video, the existing visual quality assessment (VQA) approaches are designed based on either the whole frames or the cropped patches, ignoring the fact that subjects can only access viewports. When watching 360° video, subjects select viewports through head movement (HM) and then fixate on attractive regions within the viewports through eye movement (EM). Therefore, this paper proposes a two-staged multi-task approach for viewport-based VQA on 360° video. Specifically, we first establish a large-scale VQA dataset of 360° video, called VQA-ODV, which collects the subjective quality scores and the HM and EM data on 600 video sequences. By mining our dataset, we find that the subjective quality of 360° video is related to camera motion, viewport positions and saliency within viewports. Accordingly, we propose a viewport-based convolutional neural network (V-CNN) approach for VQA on 360° video, which has a novel multi-task architecture composed of a viewport proposal network (VP-net) and viewport quality network (VQ-net). The VP-net handles the auxiliary tasks of camera motion detection and viewport proposal, while the VQ-net accomplishes the auxiliary task of viewport saliency prediction and the main task of VQA. The experiments validate that our V-CNN approach significantly advances state-of-the-art VQA performance on 360° video and it is also effective in the three auxiliary tasks. [ABSTRACT FROM AUTHOR]

We propose a new method for the visual quality assessment of 360-degree (omnidirectional) videos. The proposed method is based on computing multiple spatio-temporal objective quality features on viewports extracted from 360-degree videos. A new model is learnt to properly combine these features into a metric that closely matches subjective quality scores. The main motivations for the proposed approach are that: 1) quality metrics computed on viewports better captures the user experience than metrics computed on the projection domain; 2) the use of viewports easily supports different projection methods being used in current 360-degree video systems; and 3) no individual objective image quality metric always performs the best for all types of visual distortions, while a learned combination of them is able to adapt to different conditions. Experimental results, based on both the largest available 360-degree videos quality dataset and a cross-dataset validation, demonstrate that the proposed metric outperforms state-of-the-art 360-degree and 2D video quality metrics.

Forest-based destinations rely on quality forest landscapes as a critical resource, and recently the use of psychophysiological techniques that monitor the electrophysiological responses of the brain has gained research attention in forest landscape assessment. This study innovatively employed a mobile EEG methodology to investigate 130 participants’ attentional changes as they walked through the tourist tracks of a national park, and adopted a multi-method design triangulating the findings of a psychometric experiment, self-report measures, and expert assessments of forest landscapes. Results demonstrated a strong correlation between visitors’ attentional changes over time and on different tourist tracks, based on test–retest and alternate-form reliability testing. Moreover, the brain attention values showed some correspondence with the perceived visual quality of forest landscapes as rated by experts, and the consistency between the EEG and expert approaches was particularly evident in relation to landscapes of high and low aesthetic quality. Results provided persuasive evidence for the use of mobile EEG devices as a supplementary or alternative measure of visual quality assessment of forest landscape, and to inform landscape planning and experience design in forest-based destinations.

Perceptual encryption has received widespread attention as a technology for protecting multimedia visual information. In multimedia applications, perceptual encryption only protects a portion of the content without previewing all the information. At present, perceptual encryption is mostly oriented to conventional plain images while few methods aim at visual security measures for perceptually encrypted images. Existing solutions usually adopt well-known quality assessment metrics to measure the visual quality of encrypted images. However, they often exhibit undesired behavior on perceptually encrypted images with low quality. As a typical representation of three-dimensional scenes, light field images record the intensity and direction of light during propagation which is distinct from 2-D images. In this paper, we construct a perceptually encrypted light field image database (PE-LFID) for quality assessment based on 14 reference light field plaintext images. For each scene, we employ four encryption methods, each of which has six levels. Additionally, a novel visual security evaluation method based on PE-LFID is proposed by taking into account the local and global features of the light field images. First, we use a multi-threshold edge detection method to obtain the edge similarity in the spatial domain of the light field image. Afterwards, the epipolar plane image (EPI) generated from the angular domain of the light field image is used to calculate the gradient magnitude similarity, which is expressed as a global feature. Furthermore, the final quality prediction score is calculated by adaptively weighting between local and global features. We conduct extensive experiments on the proposed PE-LFID to assess the performance of classical and state-of-the-art IQA models. The experimental results demonstrate the effectiveness of the proposed method for visual security evaluation of perceptually encrypted light field images, as well as the scalability of PE-LFID. [ABSTRACT FROM AUTHOR]

City monuments attract visitors; especially to places with efficient viewpoints. This paper presents a case study approaches to visibility analysis, visual quality assessment and visitor perceptions of city monuments. The number of city monuments and their visibility from the closest surrounds of Svätý Jur, Pezinok and Modra towns were identified by digital terrain model, 3D landscape model and Observer points in ArcGIS. The conditions for visual quality of eight historical monuments in the city of Bratislava and their visual attractiveness from 15 selected viewpoints were evaluated by GIS GRASS, 3D landscape model and the criteria of distance, impressiveness, size and the number of visible monuments in the second approach. The visual quality of these 15 viewpoints was verified by surveying more than 300 respondents on their views of the monuments from similar locations in the third approach. These second and third approaches enabled assessment of the conformity of visual quality analysis conducted in objective-subjective and subjective ways. Certain similarity to the results was revealed in the most attractive viewpoints ranked by observers remote from the historic town. This provided views of several monuments and a panoramic view of the centre of Bratislava. Finally, the combined approaches used in assessing visual quality analysis in this study establish a comprehensive method of defining and verifying the best locations for effective sightseeing of a city’s monuments.

Surface meshes associated with diffuse texture or color attributes are becoming popular multimedia contents. They provide a high degree of realism and allow six degrees of freedom (6DoF) interactions in immersive virtual reality environments. Just like other types of multimedia, 3D meshes are subject to a wide range of processing, e.g., simplification and compression, which result in a loss of quality of the final rendered scene. Thus, both subjective studies and objective metrics are needed to understand and predict this visual loss. In this work, we introduce a large dataset of 480 animated meshes with diffuse color information, and associated with perceived quality judgments. The stimuli were generated from 5 source models subjected to geometry and color distortions. Each stimulus was associated with 6 hypothetical rendering trajectories (HRTs): combinations of 3 viewpoints and 2 animations. A total of 11520 quality judgments (24 per stimulus) were acquired in a subjective experiment conducted in virtual reality. The results allowed us to explore the influence of source models, animations and viewpoints on both the quality scores and their confidence intervals. Based on these findings, we propose the first metric for quality assessment of 3D meshes with diffuse colors, which works entirely on the mesh domain. This metric incorporates perceptually-relevant curvature-based and color-based features. We evaluate its performance, as well as a number of Image Quality Metrics (IQMs), on two datasets: ours and a dataset of distorted textured meshes. Our metric demonstrates good results and a better stability than IQMs. Finally, we investigated how the knowledge of the viewpoint (i.e., the visible parts of the 3D model) may improve the results of objective metrics. [ABSTRACT FROM AUTHOR]

Perceptual quality assessment plays a vital role in the visual communication systems owing to the existence of quality degradations introduced in various stages of visual signal acquisition, compression, transmission and display. Quality assessment for visual signals can be performed subjectively and objectively, and objective quality assessment is usually preferred owing to its high efficiency and easy deployment. A large number of subjective and objective visual quality assessment studies have been conducted during recent years. In this survey, we give an up-to-date and comprehensive review of these studies. Specifically, the frequently used subjective image quality assessment databases are first reviewed, as they serve as the validation set for the objective measures. Second, the objective image quality assessment measures are classified and reviewed according to the applications and the methodologies utilized in the quality measures. Third, the performances of the state-of-the-art quality measures for visual signals are compared with an introduction of the evaluation protocols. This survey provides a general overview of classical algorithms and recent progresses in the field of perceptual image quality assessment. [ABSTRACT FROM AUTHOR]

To decide whether the perceived quality of a mesh is influenced by a certain modification such as compression or simplification, a metric for estimating the visual quality of 3D meshes is required. Today, machine learning and deep learning techniques are getting increasingly popular since they present efficient solutions to many complex problems. However, these techniques are not much utilized in the field of 3D shape perception. We propose a novel machine learning-based approach for evaluating the visual quality of 3D static meshes. The novelty of our study lies in incorporating crowdsourcing in a machine learning framework for visual quality evaluation. We deliberate that this is an elegant way since modeling human visual system processes is a tedious task and requires tuning many parameters. We employ crowdsourcing methodology for collecting data of quality evaluations and metric learning for drawing the best parameters that well correlate with the human perception. Experimental validation of the proposed metric reveals a promising correlation between the metric output and human perception. Results of our crowdsourcing experiments are publicly available for the community. [ABSTRACT FROM AUTHOR]

Although perceptions of landscape aesthetics are currently attracting great research interest, some aspects of the topic have remained almost unexamined. This review highlights some less studied areas that are of particular importance for landscape management, with special focus on rapidly growing exurban areas. While the visual quality of the environment is undoubtedly one of the drivers that has been spurring the exurban development of rural settlements, much remains unknown about the perception of the visual quality of these settlements. Another pressing issue is the need to determine general principles of consensus formation concerning visual landscape preferences. This study concludes that in order to preserve the rural character of exurban landscapes, there is an urgent need to identify the aesthetic values that define the character of rural settlements and their importance to the stakeholder groups.

Visual quality is an important component of indoor environment quality and greatly affects inhabitants' perception of the living space. Nevertheless, the lighting design of residential buildings is usually underestimated by both designers and standards. This paper presents an integrated index for evaluating the visual quality of an indoor environment in residential buildings. The main parameters considered are daylight access, probability of glare from daylight and electric illumination, maintained illuminance, and color temperature. These aspects are evaluated throughout a whole year using a fixed set of metrics and are combined using relative weights. It was decided to also consider qualitative parameters in order to give more importance to some psychological aspects of visual comfort. Finally, the building visual quality index is obtained by a weighted average of the results of each room with the addition of the qualitative parameters. The applicability of the index was tested through the simulation of a dwelling that will be built and monitored in the context of the Comfort for Sustainable Buildings in the Alps (CASA) research project. The building visual quality index proved to be useful during the design process, allowing for improvements in both electric illumination and daylight access through a comparison of different solutions. This study could represent a step towards the holistic evaluation of indoor environment quality. [ABSTRACT FROM AUTHOR]

City monuments attract visitors; especially to places with efficient viewpoints. This paper presents a case study approaches to visibility analysis, visual quality assessment and visitor perceptions of city monuments. The number of city monuments and their visibility from the closest surrounds of Svätý Jur, Pezinok and Modra towns were identified by digital terrain model, 3D landscape model and Observer points in ArcGIS. The conditions for visual quality of eight historical monuments in the city of Bratislava and their visual attractiveness from 15 selected viewpoints were evaluated by GIS GRASS, 3D landscape model and the criteria of distance, impressiveness, size and the number of visible monuments in the second approach. The visual quality of these 15 viewpoints was verified by surveying more than 300 respondents on their views of the monuments from similar locations in the third approach. These second and third approaches enabled assessment of the conformity of visual quality analysis conducted in objective-subjective and subjective ways. Certain similarity to the results was revealed in the most attractive viewpoints ranked by observers remote from the historic town. This provided views of several monuments and a panoramic view of the centre of Bratislava. Finally, the combined approaches used in assessing visual quality analysis in this study establish a comprehensive method of defining and verifying the best locations for effective sightseeing of a city's monuments. [ABSTRACT FROM AUTHOR]

Recent developments in 3D graphics technology have led to extensive processes on 3D meshes (e.g., compression, simplification, transmission and watermarking), these processes unavoidably cause the visual perceptual degradation of the 3D objects. The existing mesh visual quality evaluation metrics either require topology constrain or fail to reflect the perceived visual quality. Meanwhile, for the 3D objects that are observed on 2D screens by the users, it is reasonable to apply image metric to assess the distortion caused by mesh simplification. We attempt to explore the efficiency of image metric for assessing the visual fidelity of the simplified 3D model in this paper. For this purpose, several latest and most effective image metrics, 2D snapshots, number and pooling algorithms are involved in our study, and finally tested on the IEETA simplification database. The statistical data allow the researcher to select the optimal parameter for this image-based mesh visual quality assessment and provide a new perspective for the design and performance assessment of mesh simplification algorithms. [ABSTRACT FROM AUTHOR]

Assessing the visual quality of super-resolution images (SRIs) is crucial for advancing algorithm development, but it remains an unsolved problem. In this paper, we present a novel reduced-reference image quality assessment (RR-IQA) method specifically suited for evaluating SRIs. Our approach leverages information from the input low-resolution (LR) image as a reference signal to extract features that are most relevant to modeling the visual quality of SRIs. We analyze the artifact characteristics of SRIs and demonstrate that features describing edge orientations, high frequency components, and textures are the most important for this task. To extract these features, we first perform structure–texture decompositions (STD) on both the SRI and its LR input, then obtain the edge orientation feature through a traditional hand-crafted approach, and deep neural networks to extract features related to high frequency components and textures. We employ a shallow multilayer perceptron (MLP) to predict an image quality score based on these quality-relevant features. To improve feature representation ability and prevent overfitting, we pretrain the feature extraction module using a large number of unlabeled samples, which accounts for over 99.8% of the total model parameters. We use precious samples with mean opinion score (MOS) labels to train a high-quality shallow MLP predictor. Our experimental results show that the proposed method outperforms classical and state-of-the-art models. • Assessing the visual quality of SR images by considering scene structure and texture. • Leveraging beneficial information from the low-resolution input images. • Exploiting both deep features and shallow features for SR IQA. • Pre-training the majority of learnable parameters in a self-supervised manner. • Experimental results show the advantage of the proposed model over SOTA ones. [ABSTRACT FROM AUTHOR]

In image coding applications, quantitative quality metrics which approximate the perceived quality of an image can be used for evaluating the performance of coders, designing new coders and optimizing existing coders. In this paper, we consider images of high quality levels for which most of the errors are in the threshold region of perception, and perceptible errors are sparsely distributed. Two different methodologies are used; first, we use an objective Picture Quality Scale (PQS) which combines partial measures, denoted distortion factors, of random as well as structured perceived distortions due to coding. We also consider an alternative approach, applicable at high quality, that is based on a multi-channel vision model, the Visible Differences Predictor (VDP) proposed by Scott Daly. The VDP produces a probability detection map identifying regions in which errors are sub-threshold, threshold and supra-threshold. For the PQS, since distortion factors due to structured errors and errors in the vicinity of high contrast image transitions are most important at high quality, these two factors are analyzed to compute spatial distortion maps of their contributions. This paper compares the spatial distortion maps produced by both methods for high quality still images to experimental observations. Global values for the quality have been obtained by integrating the factor images obtained using PQS and yield a correlation of 0.9 with mean opinion of score values for JPEG, Subband and Wavelet coded images.

Most of the forested lands in the south-eastern United States were largely the result of the frequent, low intensity, non-lethal fires that swept through the pre-settlement forests. In the absence of fire, forested stands develop a thick undergrowth of broad-leaved species and herbaceous vegetation. In this study, we compared the influence of prescribed fire on the understory vegetative cover of loblolly pine stands in relation to visual quality assessment. To examine the visual quality of prescribed fire management, a box-counting method was used to analyze photographs of prescribed fire management. The photographs were taken from: a) oneyear fire return interval, b) two-year fire return interval, c) three-year fire return interval, and d) no-burn. The objectives of this study were: 1) develop a comparison of aesthetic value of the different fire return intervals of fire management with areas not experiencing prescribed fire; and 2) provide an estimate of enhancing visual quality of forest stands with prescribed fire management. The results showed that one-year interval prescribed fire application stands had more abundant grass cover (Andropogon sp.) than two- and three-year interval treatments. There was a significant decrease in grass cover between one-year and two-year prescribed burning. A positive relationship was also found between frequency of fire treatment and abundance of grass. No burn areas were covered by woody vegetation which may have a negative impact on scenic beauty and visual quality. [ABSTRACT FROM AUTHOR]

Road infrastructure is a dominant element of urban landscapes which influences its visual identity. In the past decades, landscape visual quality issues have become increasingly significant in the contemporary approach in road infrastructure planning. At the same time, the European Landscape Convention provides a means of achieving "landscape democracy" and promotes improved public participation in the spatial and urban planning as well as in road planning. The visual quality assessment which takes into account the aesthetic demands of public perception has evolved into a required study for green infrastructure planning where roadside greenspace has an important role. This paper presents the results of assessing the visual quality through holistic approach which combines quantitative public preference surveys and landscape features inventories. The main direction is to predict visual quality based on the presence of quantifiable biophysical structure of the green spaces along urban roads. The different types of urban biotopes, with various biophysical structure, were the spatial units for the survey. An application based on 38 roadside green spaces in the metropolitan area of Belgrade is presented. On the basis of the visual quality assessment and preferences of urban users, the results will be useful to improvement modality for decision making support and public participation in the processes of urban landscape planning. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope. Second, inspired by existing studies from neuroscience that high-order derivatives can capture image texture, we adopt four filters with different directions to compute gradient maps from the luminance map. These gradient maps are then used to extract the second-order derivatives by local binary pattern. We further extract the texture feature by the histogram of high-order derivatives in global scope. Finally, support vector regression is applied to train the mapping function from quality-aware features to subjective ratings. Experimental results on the public large-scale SCI database show that the proposed NRLT can achieve better performance in predicting the visual quality of SCIs than relevant existing methods, even including some full reference visual quality assessment methods. [ABSTRACT FROM PUBLISHER]

Although Middle Black Sea region of Turkey harbors various natural and cultural aspects, the scientific researches about such a rich source of the region are still insufficient. The present study was conducted to assess the visual landscape quality of natural and cultural beauties of Niksar town and close surroundings and to investigate the effects of landscape components on visual quality. The type of landscape with the greatest visual quality value was identified as Rocky Habitat Landscape 2 (r=4.386) and it was followed respectively by Agricultural Landscape 5 (r=4.227) and Natural Landscape 3 (r=4.137). It was observed that all of the landscape parameters considered in this study (naturalness, variety, vividness, being interesting, harmony, originality, cultural value) significantly affected visual quality values. Besides, perceptibility also had significant effects on visual quality of the landscapes. Finally recommendations were provided for the future use of natural resources of the region. [ABSTRACT FROM AUTHOR]

Visual quality is an important component of indoor environment quality and greatly affects inhabitants’ perception of the living space. Nevertheless, the lighting design of residential buildings is usually underestimated by both designers and standards. This paper presents an integrated index for evaluating the visual quality of an indoor environment in residential buildings. The main parameters considered are daylight access, probability of glare from daylight and electric illumination, maintained illuminance, and color temperature. These aspects are evaluated throughout a whole year using a fixed set of metrics and are combined using relative weights. It was decided to also consider qualitative parameters in order to give more importance to some psychological aspects of visual comfort. Finally, the building visual quality index is obtained by a weighted average of the results of each room with the addition of the qualitative parameters. The applicability of the index was tested through the simulation of a dwelling that will be built and monitored in the context of the Comfort for Sustainable Buildings in the Alps (CASA) research project. The building visual quality index proved to be useful during the design process, allowing for improvements in both electric illumination and daylight access through a comparison of different solutions. This study could represent a step towards the holistic evaluation of indoor environment quality.