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Introduction: While there is a call for smart and sustainable housing in general and for older adults in particular, little attention is paid to identifying the determinants of such housing and their extent of influence on the quality of life (QoL) of older adults. This study addresses the above gap by re-defining the criteria for house quality assessment, taking into account new needs of older inhabitants, while concerning digital assistive technologies. Methods: This research uses various methods to identify and validate housing-related criteria and metrics, resulting in a transparent multi-criteria evaluation framework that accounts for the spatial needs of older adults. These include recommendations for multi-criteria decision-making method (MCDM/A), expert workshop to develop new metrics and validate sub-criteria, expert survey to prioritize criteria and sub-criteria and interviews with three employees in the construction-services sector in the Netherlands, to gain knowledge on smart and healthy environments. Results and Discussion: The results show that age-friendliness of housing function is the most significant criterion, while availability of housing modifications for seniors most important sub-criterion. Our findings can benefit architects in designing improved age- friendly spaces, older adults in evaluating their dwellings and researchers from the field of architecture in selecting most relevant method for their study. [ABSTRACT FROM AUTHOR]

Mainstream image/video coding standards, exemplified by the state-of-the-art H.266/VVC, AVS3, and AV1, follow the block-based hybrid coding framework. Due to the block-based framework, encoders designed for these standards are easily optimized for peak signal-to-noise ratio (PSNR) but have difficulties optimizing for the metrics more aligned to perceptual quality, e.g., multi-scale structural similarity (MS-SSIM), since these metrics cannot be accurately evaluated at the small block level. We address this problem by leveraging inspiration from the end-to-end image compression built on deep networks, which is easily optimized through network training for any metric as long as the metric is differentiable. We compared the trained models using the same network structure but different metrics and observed that the models allocate rates in different ratios. We then propose a distillation method to obtain the rate allocation rule from end-to-end image compression models with different metrics and to utilize such a rule in the block-based encoders. We implement the proposed method on the VVC reference software—VTM and the AVS3 reference software—HPM, focusing on intraframe coding. Experimental results show that the proposed method on top of VTM achieves more than 10% BD-rate reduction than the anchor when evaluated with MS-SSIM or LPIPS, which leads to concrete perceptual quality improvement. [ABSTRACT FROM AUTHOR]

IntroductionWhile there is a call for smart and sustainable housing in general and for older adults in particular, little attention is paid to identifying the determinants of such housing and their extent of influence on the quality of life (QoL) of older adults. This study addresses the above gap by re-defining the criteria for house quality assessment, taking into account new needs of older inhabitants, while concerning digital assistive technologies.MethodsThis research uses various methods to identify and validate housing-related criteria and metrics, resulting in a transparent multi-criteria evaluation framework that accounts for the spatial needs of older adults. These include recommendations for multi-criteria decision-making method (MCDM/A), expert workshop to develop new metrics and validate sub-criteria, expert survey to prioritize criteria and sub-criteria and interviews with three employees in the construction-services sector in the Netherlands, to gain knowledge on smart and healthy environments.Results and DiscussionThe results show that age-friendliness of housing function is the most significant criterion, while availability of housing modifications for seniors most important sub-criterion. Our findings can benefit architects in designing improved age- friendly spaces, older adults in evaluating their dwellings and researchers from the field of architecture in selecting most relevant method for their study.

Face frontalization is the process of synthesizing realistic and identity-preserving frontal views from face images in different poses and is an essential preprocessing step for face recognition. However, for side faces wearing glasses, the previous frontalization algorithms will distort the glasses after face reconstruction, affecting the image's perceived quality and subsequent face recognition. Therefore, this paper first removes glasses, a factor that will cause distortion in face frontalization, and designs the perceptual and pixel-level face image quality assessment modules to improve the face frontalization performance. On the one hand, by constructing a saliency gradient, the pixel-level quality of face images is calculated and guides the network to generate frontal face images that are more conducive to face recognition. On the other hand, in order to obtain the perceptual quality for face image, the natural face images are used to construct a high-quality feature space, and the Bhattacharyya distance between it and the generated image is calculated to ensure the perceptual quality of the generated frontal image. Finally, the GAN network is used to generate a frontal face image that can consider both recognizability and perceptual quality. Quantitative and qualitative evaluations on controlled and in-the-wild databases show that our method outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

Clipping is one of the non-linear distortions commonly introduced due to microphone saturation during speech recording. Present work focuses on the effect of clipping in the task of prosody modification. Since, $F_{0}$ contour and duration are the important prosodic parameters, the present work studies the effect of clipping in the manipulation of $F_{0}$ and duration of a given speech. Epoch based prosody modification is considered as the popular method to generate waveforms with good perceptual quality by scaling $F_{0}$ contour and duration of the given speech by fixed scaling factors. Therefore, present work studies the effect of waveform clipping on the perceptual quality of prosody modified speech. Deviations in the estimation of epochs (which are used as the analysis pitch marks) and method used for generating the waveform are the two ways wherein perceptual quality in epoch based prosody modification can be compromised. The work proposed in this paper examines, effect of clipping on the aforesaid stages of epoch based prosody modification affecting the perceptual quality of the generated speech. Zero frequency filtering (ZFF), a simple and popular method, is chosen as the epoch estimation algorithm for epoch based prosody modification presented in the paper. Based on comparative epoch estimation performance analysis carried out by introducing various amplitude clipping levels, epoch identification rates are confirmed to be unchanged, irrespective of the level of clipping distortions present. However, due to saturation in the waveform samples, the waveform generation stage of the prosody modification was observed to be affected to the level which was proportional to the clipping distortions present in the signal. A variational mode decomposition (VMD) based signal approximation of the prosody modified speech is proposed to reduce the non-linear effect due to clipping. At the gross level, the re-estimated speech signal obtained from the VMD modes observed to have improved the perceptual quality of the pitch and duration modified speech. The improved perceptual quality of VMD based re-estimation of prosody modified speech was confirmed from subjective and NIST-STNR based objective assessments. Further, VMD based refinement is proposed as an alternative to local mean subtraction for trend removal in conventional ZFF of speech for the accurate epoch estimation. Comparative performance analysis carried out on CMU arctic database, confirmed improvement in the identification accuracy for the epochs estimated by using VMD based trend removal in ZFF algorithm.

No-reference image quality assessment (NR-IQA), which aims to estimate the perceptual quality of a degraded image without accessing the corresponding original image, is a key challenge in low-level computer vision. Recent advances in deep learning have enabled the development of high-performance NR-IQA methods. However, such methods are limited, as they are highly dependent on the training dataset. Recognizing this limitation and avoiding task-specific training, an alternative method has been proposed that employ pre-trained visual language models for zero-shot NR-IQA; however, this approach does not provide any basis for decision-making and is not explainable. In this study, we propose ZEN-IQA, a new zero-shot and explainable NR-IQA method. Utilizing a new approach involving carefully constructed prompt pairs and triplets makes the evaluation process more intuitive and easier to understand. Our comparative analysis reveals that ZEN-IQA not only has high interpretability, but also outperforms methods using handcrafted features and state-of-the-art deep learning methods trained based on datasets that differ from the test set. We also applied ZEN-IQA to images before and after image processing and conducted experiments to evaluate how perceptual quality changes with image processing. The code is publicly available at https://github.com/mtakamichi/ZEN-IQA.

In recent years, video compression has emerged as a focal point of considerable interest. Nevertheless, the predominant focus of existing methods lies in the meticulous reconstruction of videos with high fidelity, often at the expense of prioritizing the perceptual visual comfort experienced by human viewers. This paper presents an innovative learnable perceptual video compression method that extends the capabilities of current codecs. It enhances their perceptual coding proficiency by delving into the significance of local semantics and foreground objects in the context of human vision. Incorporating local semantics into the coding system involves the utilization of a region-wise contrastive learning objective, compelling the encoder to extract information pertinent to semantics. To safeguard foreground objects from corruption during compression, we prioritize minimal distortion in the foreground regions. This is achieved by employing an off-the-shelf visual saliency model for the precise detection of these regions. In an effort to augment the representation capacity of the convolution operator employed in our compression network, we introduce a recurrent information-based adaptive convolution block, thereby enhancing compression efficiency. Comprehensive experimental results validate the efficacy of our approach in achieving superior perceptual coding performance.

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The cumulative effects of network transmission link imperfections and realtime limitations of video data result in multiple challenges for video transmission in massive millimeter wave (mmwave) network. The challenges, together with the increasing expectations of users for goodquality videos, further extend the complexity particularly in dense areas. To address the issues this work proposes perceptual quality aware video encoding (PQAVE) scheme in two phases: in phase 1 an effective perceptual quality encoding method leveraging low-rank approximation to reduce overall video size is done. In phase 2, a novel bitstream optimization technique is introduced to improve perceptual quality of videos. Experiment are conducted using standard video dataset show the proposed PQAVE model attain better bit error rate (BER), symbol error rate (SER), error vector magnitude (EVM), and coding gain in comparison with existing perceptual video encoding scheme. [ABSTRACT FROM AUTHOR]

Image denoising-removal of additive white Gaussian noise from an image-is one of the oldest and most studied problems in image processing. Extensive work over several decades has led to thousands of papers on this subject, and to many well-performing algorithms for this task. Indeed, 10 years ago, these achievements led some researchers to suspect that "Denoising is Dead, in the sense that all that can be achieved in this domain has already been obtained. However, this turned out to be far from the truth, with the penetration of deep learning (DL) into the realm of image processing. The era of DL brought a revolution to image denoising, both by taking the lead in today's ability for noise suppression in images, and by broadening the scope of denoising problems being treated. Our paper starts by describing this evolution, highlighting in particular the tension and synergy that exist between classical approaches and modern artificial intelligence (AI) alternatives in design of image denoisers. The recent transitions in the field of image denoising go far beyond the ability to design better denoisers. In the second part of this paper we focus on recently discovered abilities and prospects of image denoisers. We expose the possibility of using image denoisers for service of other problems, such as regularizing general inverse problems and serving as the prime engine in diffusion-based image synthesis. We also unveil the (strange?) idea that denoising and other inverse problems might not have a unique solution, as common algorithms would have us believe. Instead, we describe constructive ways to produce randomized and diverse high perceptual quality results for inverse problems, all fueled by the progress that DL brought to image denoising. This is a survey paper, and its prime goal is to provide a broad view of the history of the field of image denoising and closely related topics in image processing. Our aim is to give a better context to recent discoveries, and to the influence of the AI revolution in our domain. [ABSTRACT FROM AUTHOR]

The overpasses and the terrain under them in Chongqing, a mountainous city in China, are complex and diverse, and some spaces under the overpasses are integrated and reconstructed into the underpass green space for citizens to stroll about or have a rest. From the perspective of visitor perception, this paper constructs a perception evaluation system of the environmental characteristics of underpass green space in mountainous cities from the following five environmental perception dimensions: path organization, security, aesthetic value, physical environment, activities and cultural. The IPA-Kano model is used to quantify environmental perception, and the main environmental factors affecting the improvement of recreation satisfaction of underpass green space in three types of terrain are explored, with a view to improving the environment and service functions of underpass green spaces in high-density interchange networks in mountainous cities, and enhancing the attractiveness of underpass green spaces. It can be found from the study that: (1) Among the five environmental perception dimensions, visitors pay more attention to the physical environment quality of the underpass green space and their physical and psychological activity experience, while their demands for visual senses are relatively low. Due to the deficiency or lack of leisure facilities, sports facilities, children's playgrounds and amusement equipment, the dimension of "activities and cultural perception" of the underpass green space has the lowest scores of all. (2) The existing sites, facilities and landscape resources of the underpass green space, different terrain types and underpass environment are the important reasons that affect the performance of environmental perception factors and their priority ranking results. (3) The improvement of security of the arrival path or sports facilities is beneficial to improve visitor satisfaction of underpass green space of three types of terrain. The number of environmental factors to be optimized of the three types of terrain are ranked as: mountainous green space > flat green space > concave green space. Among them, four environmental factors have a high priority in two kinds of underpass green space, which are the distribution and quantity of leisure facilities, the effect of noise reduction and sound insulation, the adequacy of activity venues and the distribution and quantity of sports facilities. Finally, according to the particularity of the underpass environment and the characteristics of three types of terrain, this paper puts forward some suggestions for optimizing the service function of underpass green space from five perceptual dimensions. [ABSTRACT FROM AUTHOR]

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Deep neural networks have become very popular for solving many problems in computer vision. Super-resolution (SR) is a particularly challenging task since new information must be created at increased resolution, possibly leading to visual artefacts or incoherent texture. In the context of remote sensing, this image restoration technique has great potential for synthesizing high-resolution (HR) data from low-resolution (LR) images. While there are multiple methods that enhance the perceptual quality of SR images, most of them fail to recover detailed information from aerial imagery. One of the main reasons for that is the difficulty in defining a 'good-looking' image from the perspective of the machine. In this work, we propose an end-to-end training procedure that unifies networks related to different tasks: an SR module based on generative adversarial networks (GANs) and a semantic segmentation module. Our claim is that by including a classification loss when estimating the HR image, the GAN generator produces more coherent structures and textural information, synthesizing, therefore, more realistic images according to perception-based scores. Our experimental results show that the proposed method is capable of improving perceptual outputs of deep-learning oriented networks with a small training overhead, surpassing multiple state-of-the-art super-resolution methods. Our code is available at . [ABSTRACT FROM AUTHOR]

Speech enhancement helps in eliminating the environmental noises from the communication signals. The main intention of the augmentation system is to develop the perceptual quality of communication or speech. For this purpose, various filtering schemes, spectral restoration models and speech models were implemented. In order to improve the odds of reducing noise and restoring the original signal, artificial intelligence (AI) and machine learning algorithms (MLA) were included into every sector. Deep transfer learning was used in this work to remove noise from the data and restore the original signals. This proposed approach includes a filtration scheme instead of using a convolution layer in the RESNET-50 architecture. The filters tested for speech enhanced deep learning models are modified Kalman filter and enhanced wiener filter. The performance metrics were calculated between various algorithms and proposed models to identify which approaches to follow the better way result obtained. The performance metrics compared PESQ, LSD and segSNR for different low signal to noise ratio conditions. • Speech enhancement helps in eliminating the environmental noises from the communication signals. • In order to improve odds of reducing noise fatigue in speech signal, Deep learning (DL) algorithms were included. • This proposed approach includes a filtration scheme instead of using a convolution layer in the RESNET-50 architecture. [ABSTRACT FROM AUTHOR]

With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study's image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people's average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images. [ABSTRACT FROM AUTHOR]

In single image super resolution tasks, distortion measurement (such as PSNR, SSIM) and perceptual quality (such as PI, NIQE) are contradictory, and methods that perform well in perceptual quality often perform poorly in distortion measurement, and vice versa. In this article, we propose a method of balancing the two, which is divided into three stages. Firstly, this article proposes an image super-resolution model NLSAV2 that focuses on PSNR and SSIM metrics. The entire NLSAV2 consists of three modules: shallow feature extraction, deep feature extraction, and high-quality image reconstruction. In the shallow feature extraction module, non-local sparse attention is used to identify the most abundant feature information in mapping input from low-dimensional space to high-dimensional space, and the deep feature extraction module mainly consists of residual Swin Transformer V2 Block. Then, NLSAV2 is used as a generator and a relative discriminator is introduced to further train the model, which is called NLSAV2-GAN. The experimental results indicate that NLSAV2 and NLSAV2-GAN exhibit advantages in distortion measurement and perceptual quality respectively. Finally, network interpolation and image interpolation strategies are used to continuously adjust the reconstruction style and smoothness to achieve a balance between the two. • A solution to balance high PSNR and image perception quality is proposed. • A non-local sparse attention method is added to the shallow feature extraction module to identify the location with the most abundant feature information. • An image super resolution model NLSAV2 is proposed, and a relative discriminator is introduced to obtain better perception quality. [ABSTRACT FROM AUTHOR]

Aims Taking strategy of urban branding is considered the basic need of cities to survive in the global economy. For this purpose, building and managing the city image is necessary because it is through perception and image that actions are formed. The purpose of this research was to investigate the relationship between effective components on the image in the form of a case study including 15 shopping centers in Tabriz. Methodology The current research was conducted with the field method and exploratory analysis type in 1401 in the statistical population of Tabriz citizens. By conducting documentary studies in the field of urban branding, the constructive components were identified and formulated in the form of a five-factor model. The relationship between variables was tested using structural equation modeling with partial least squares and data were analyzed using SmartPLS3 software. Findings The At a significance level of 0.01, "perceptual quality" and "awareness" have a direct effect on the "mental image" and at level of 0.05, "personal characteristics" have a direct effect on the "mental image". At level of 0.01, "mental image" has a direct effect on "loyalty". 73.8% of the total effect of "perceptual quality" variable, 43.3% of "personal characteristics'; 32.1% of "awareness" on "loyalty" is explained through the mediating variable of "mental image". Conclusion "Mental image" as the center of gravity in urban branding is directly influenced by three components; "perceptual quality", "personal characteristics" and "awareness". Among these "perceptual quality" is considered the most influential component. "Loyalty" as the final goal of branding is not directly related to aforementioned components and influencing it is possible through the mediation of "mental image". Therefore, the application of design measures in order to improve "perceptual quality" causes the formation of a powerful "mental image" of shopping spaces, which in turn leads to "loyalty" and provides the grounds for branding of such spaces. [ABSTRACT FROM AUTHOR]

The paper is devoted to issues of perceptual quality of images after compression/recovery processing. In contrast to the known approaches, our investigation has a fundamentally different background. We propose the perceptual coding synthesis based on image representations most adequate for human perception, but not on the most appropriate perceptual metrics. Namely, we base our approach on the previously developed biologically motivated representation of images by samples of counts (sampling representations). The paper gives a short overview of this concept. Since the sampling representations are essentially random, the approach proposed differs from the others in its fundamentally statistical orientation. In this respect, it is very close to the generative model-based approaches currently popular in machine learning. The basic scheme of the approach proposed is focused on generative models in the form of sampling representations, discussed in detail in the paper. Since within the framework of generative models the main instruments are the probability distributions of input/output data, the focus of our approach is also on the probability distributions of counts of sampling representations. As the specific feature of sampling representations is the fact that their complete statistical description has the form of a product of the distribution densities of individual counts, the goal of the approach proposed is, in essence, to estimate these densities. Within the scheme of the approach proposed such estimates concern the parameters of the densities chosen from a certain parametric family of probability distributions. In this regard, the choice of the model of the parametric family that is adequate to the visual perception is extremely important. So, the paper discusses in detail the choice of a parametric family in the form of a system of receptive fields. The theoretical aspects of the approach proposed are supplemented by the results of computer simulation. In conclusion, a summary of the paper discussion is given. [ABSTRACT FROM AUTHOR]

The overpasses and the terrain under them in Chongqing, a mountainous city in China, are complex and diverse, and some spaces under the overpasses are integrated and reconstructed into the underpass green space for citizens to stroll about or have a rest. From the perspective of visitor perception, this paper constructs a perception evaluation system of the environmental characteristics of underpass green space in mountainous cities from the following five environmental perception dimensions: path organization, security, aesthetic value, physical environment, activities and cultural. The IPA-Kano model is used to quantify environmental perception, and the main environmental factors affecting the improvement of recreation satisfaction of underpass green space in three types of terrain are explored, with a view to improving the environment and service functions of underpass green spaces in high-density interchange networks in mountainous cities, and enhancing the attractiveness of underpass green spaces. It can be found from the study that: (1) Among the five environmental perception dimensions, visitors pay more attention to the physical environment quality of the underpass green space and their physical and psychological activity experience, while their demands for visual senses are relatively low. Due to the deficiency or lack of leisure facilities, sports facilities, children’s playgrounds and amusement equipment, the dimension of “activities and cultural perception” of the underpass green space has the lowest scores of all. (2) The existing sites, facilities and landscape resources of the underpass green space, different terrain types and underpass environment are the important reasons that affect the performance of environmental perception factors and their priority ranking results. (3) The improvement of security of the arrival path or sports facilities is beneficial to improve visitor satisfaction of underpass green space of three types of terrain. The number of environmental factors to be optimized of the three types of terrain are ranked as: mountainous green space > flat green space > concave green space. Among them, four environmental factors have a high priority in two kinds of underpass green space, which are the distribution and quantity of leisure facilities, the effect of noise reduction and sound insulation, the adequacy of activity venues and the distribution and quantity of sports facilities. Finally, according to the particularity of the underpass environment and the characteristics of three types of terrain, this paper puts forward some suggestions for optimizing the service function of underpass green space from five perceptual dimensions.

Existing Quality Assessment (QA) algorithms consider identifying “black-holes” to assess perceptual quality of 3D-synthesized views. However, advancements in rendering and inpainting techniques have made black-hole artifacts near obsolete. Further, 3D-synthesized views frequently suffer from stretching artifacts due to occlusion that in turn affect perceptual quality. Existing QA algorithms are found to be inefficient in identifying these artifacts, as has been seen by their performance on the IETR dataset. We found, empirically, that there is a relationship between the number of blocks with stretching artifacts in view and the overall perceptual quality. Building on this observation, we propose a Convolutional Neural Network (CNN) based algorithm that identifies the blocks with stretching artifacts and incorporates the number of blocks with the stretching artifacts to predict the quality of 3D-synthesized views. To address the challenge with existing 3D-synthesized views dataset, which has few samples, we collect images from other related datasets to increase the sample size and increase generalization while training our proposed CNN-based algorithm. The proposed algorithm identifies blocks with stretching distortions and subsequently fuses them to predict perceptual quality without reference, achieving improvement in performance compared to existing no-reference QA algorithms that are not trained on the IETR dataset. The proposed algorithm can also identify the blocks with stretching artifacts efficiently, which can further be used in downstream applications to improve the quality of 3D views. Our source code is available online: https://github.com/sadbhawnathakur/3D-Image-Quality-Assessment. [ABSTRACT FROM AUTHOR]

Compressed Sensing (CS) is a groundbreaking paradigm in image acquisition, challenging the constraints of the Nyquist–Shannon sampling theorem. This enables high-quality image reconstruction using a minimal number of measurements. Neural Networks' potent feature induction capabilities enable advanced data-driven CS methods to achieve high-fidelity image reconstruction. However, achieving satisfactory reconstruction performance, particularly in terms of perceptual quality, remains challenging at extremely low sampling rates. To tackle this challenge, we introduce a novel two-stage image CS framework based on latent diffusion, named LD-CSNet. In the first stage, we utilize an autoencoder pre-trained on a large dataset to represent natural images as low-dimensional latent vectors, establishing prior knowledge distinct from sparsity and effectively reducing the dimensionality of the solution space. In the second stage, we employ a conditional diffusion model for maximum likelihood estimates in the latent space. This is supported by a measurement embedding module designed to encode measurements, making them suitable for a denoising network. This guides the generation process in reconstructing low-dimensional latent vectors. Finally, the image is reconstructed using a pre-trained decoder. Experimental results across multiple public datasets demonstrate LD-CSNet's superior perceptual quality and robustness to noise. It maintains fidelity and visual quality at lower sampling rates. Research findings suggest the promising application of diffusion models in image CS. Future research can focus on developing more appropriate models for the first stage. • Latent diffusion-based architecture tackles image compressive sensing challenges. • Pre-trained generative networks model the prior information of natural images. • Diffusion models maximize likelihood estimation in a low-dimensional latent space. • The perceptual quality of reconstructed images improves at low sampling rates. [ABSTRACT FROM AUTHOR]

Cultivated and wild Rosa roxburghii Tratt were investigated to determine the intrinsic causes of the quality differences. The cultivated fruit, Guinong No. 5 (GNNF) was larger, heavier, and had less color difference. Its juice contained higher levels of soluble solids and tannins. The wild variety (WRRT) had higher concentrations of condensed tannins, polyphenols and flavonoids. Artificial sensory and electronic taste tests indicated GNNF had a lower astringent and bitter taste compared to WRRT. A total of 80 flavour substances were identified by gas chromatography‒mass spectrometry (GC-MS). The relative contents of esters, organic acids, ketones, alcohols, aldehydes and aromatic hydrocarbons were higher in GNNF than in WRRT. Caffeylalcohol, catechin, quinidine, pantothenic acid and five bitter amino acids were high in GNNF. The high contents of bitter substances such as limonin, daidzein, and rutaevin were responsible for the quality differences between WRRT and GNNF. • The primary sensory distinction between WRRT and GNNF is bitter flavor. • The primary aromatic compounds found in WRRT are esters and terpenes. • The primary aromatic compounds found in GNNF are esters. • The bitterness of WRRT was attributed to limonin, rutaevin and daidzein. • The overall flavor impact of GNNF was greater than WRRT. [ABSTRACT FROM AUTHOR]

Blind or no-reference video quality assessment of user-generated content (UGC) has become a trending, challenging, heretofore unsolved problem. Accurate and efficient video quality predictors suitable for this content are thus in great demand to achieve more intelligent analysis and processing of UGC videos. Previous studies have shown that natural scene statistics and deep learning features are both sufficient to capture spatial distortions, which contribute to a significant aspect of UGC video quality issues. However, these models are either incapable or inefficient for predicting the quality of complex and diverse UGC videos in practical applications. Here we introduce an effective and efficient video quality model for UGC content, which we dub the Rapid and Accurate Video Quality Evaluator (RAPIQUE), which we show performs comparably to state-of-the-art (SOTA) models but with orders-of-magnitude faster runtime. RAPIQUE combines and leverages the advantages of both quality-aware scene statistics features and semantics-aware deep convolutional features, allowing us to design the first general and efficient spatial and temporal (space-time) bandpass statistics model for video quality modeling. Our experimental results on recent large-scale UGC video quality databases show that RAPIQUE delivers top performances on all the datasets at a considerably lower computational expense. We hope this work promotes and inspires further efforts towards practical modeling of video quality problems for potential real-time and low-latency applications.

This article first provides a new Viewport-based OmniDirectional Video Quality Assessment (VOD-VQA) database, which includes eighteen salient viewport videos extracted from the original OmniDirectional Videos (ODVs), and corresponding 774 impaired samples generated by compressing the raw viewports using a variety of combinations of its Spatial (frame size $s$), Temporal (frame rate $t$), and Amplitude (quantization stepsize $q$) Resolutions (STAR). Total 160 subjects have assessed the processed viewport videos rendered on the head mounted display (HMD) when they stabilize their fixations. We then have formulated an analytical model to connect the perceptual quality of a compressed viewport video with its STAR variables, noted as the $Q^{{\mathsf {VP}}}_{\tt {STAR}}$ index. All four model parameters can be predicted using linearly weighted content features, making the proposed metric generalized to various contents. This model correlates well with the mean opinion scores (MOSs) collected for processed viewport videos, having both the Pearson Correlation Coefficient and Spearman’s Rank Correlation Coefficient (SRCC) at 0.95 according to an independent validation test, yielding the state-of-the-art performance in comparison to those popular objective metrics (e.g., Weighted to Spherically uniform (WS)-Peak Signal to Noise Ratio (PSNR), WMS-SSIM, Video Multimethod Assessment Fusion (VMAF), Feature SIMilarity Index (FSIM), and Visual Saliency based IQA Index (VSI)). Furthermore, this viewport-based quality index $Q^{{\mathsf {VP}}}_{\tt {STAR}}$ is extended to infer the overall ODV quality, a.k.a., $Q^{{\mathsf {ODV}}}_{\tt {STAR}}$ , by linearly weighing the saliency-aggregated qualities of salient viewports and the quality of quick-scanning (or non-salient) area. Experiments have shown that inferred $Q^{{\mathsf {ODV}}}_{\tt {STAR}}$ can accurately predict the MOS with competitive performance to the state-of-the-art algorithm using another four independent and third-party ODV assessment datasets. All related materials are made publicly accessible at https://vision.nju.edu.cn/20/86/c29466a467078/page.htm for reproducible research. [ABSTRACT FROM AUTHOR]

Instead of streaming the entire omnidirectional video that induces a waste of bandwidth, the tile-based viewport adaptive streaming is preferred in practice. Tiles within users’ viewport are transmitted in high quality, while tiles elsewhere are delivered in lower quality to reduce the network bandwidth consumption. In this setup, when users change their viewport, they may potentially be disturbed by the exposure of low-quality tiles and the subsequent quality refinement in a period of delay. We defined this phenomenon in the viewport as a Dynamic Quality Boundary (DQB) artifact, as there exists a clear boundary between low-quality tiles and high-quality tiles which appears, moves, and disappears along with the change of users’ viewport. The DQB artifact is a specific quality degradation in the viewport-adaptive omnidirectional video streaming whose impact on users’ perceptual quality has not been adequately studied. In this paper, we focused on investigating and modeling its impact on the quality perception of users when streaming omnidirectional videos. More specifically, systematic subjective quality evaluation experiments towards DQB artifact were designed and conducted. Accordingly, a perceptual quality metric was proposed considering the quality of tiles, the proportion of low-quality tiles, and the refinement duration. Experimental results, measured in various aspects, showed that the proposed metric can accurately predict users’ perceptual quality of the tile-based viewport-adaptive omnidirectional video streaming service. [ABSTRACT FROM AUTHOR]

Obtaining clear images of underwater scenes with descriptive details is an arduous task. Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors. Consequently, a need for a system that produces clear images for underwater image study has been necessitated. To overcome problems in resolution and to make better use of the Super-Resolution (SR) method, this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network (AlphaGAN) model, named Alpha Super Resolution Generative Adversarial Network (AlphaSRGAN). The model put forth in this paper helps in enhancing the quality of underwater imagery and yields images with greater resolution and more concise details. Images undergo pre-processing before they are fed into a generator network that optimizes and reforms the structure of the network while enhancing the stability of the network that acts as the generator. After the images are processed by the generator network, they are passed through an adversarial method for training models. The dataset used in this paper to learn Single Image Super Resolution (SISR) is the USR 248 dataset. Training supervision is performed by an unprejudiced function that simultaneously scrutinizes and improves the image quality. Appraisal of images is done with reference to factors like local style information, global content and color. The dataset USR 248 which has a huge collection of images has been used for the study is composed of three collections of images--high (640×480) and low (80×60, 160×120, and 320×240). Paired instances of different sizes--2×, 4× and 8×--are also present in the dataset. Parameters likeMean Opinion Score (MOS), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) and Underwater Image Quality Measure (UIQM) scores have been compared to validate the improved efficiency of our model when compared to existing works. [ABSTRACT FROM AUTHOR]

360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360-degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user's familiarity with VR and user's interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.

Depth-Image-Based-Rendering (DIBR), as one important technique in 3D video system, can be used to generate virtual views. Unfortunately, the DIBR algorithms will introduce various distortions and induce an annoying viewing experience. And it has been proved that traditional 2D assessment quality metrics are not suitable for the DIBR-synthesized views. In this paper, we propose a novel approach to assess the quality of DIBR-synthesized images. The proposed method mainly considers three kinds of DIBR-related distortions: holes distortion, strip-sharped distortion and global sharpness. Holes and strip distortions as two local features are used to characterize the local quality of DIBR-synthesized image, respectively. For the global sharpness we consider the Just Notice Difference (JND) model of human eyes and use it to extract the JND-based global difference for analyzing the global quality. Finally, we combine the holes distortion evaluation, strip distortion evaluation and global quality to infer the overall perceptual quality. Extensive experiments indicate that our method achieves higher accuracy of quality prediction than most competing metrics.

The reconstructed image of the size-invariant visual cryptography (VC) is inevitably accompanied by the loss of secret image information and the degradation of perceptual quality. Here, the halftone technique comes to the forefront since it can realistically simulate the grayscale image from a discrete binary image. Thus, by combining VC sharing with grayscale image halftone technique, this paper proposes a size-invariant VC scheme for grayscale image underpinned by the efficient direct binary search (EDBS) algorithm, in which the multi-pixel encryption VC sharing is adopted into the EDBS halftone process. Through local optimizations and global iterations, the optimal reconstructed image is obtained. To further enhance the contrast of the reconstructed image with limited computational power, the image information is probabilistically extracted according to the inverse mapping in the codebook. It is theoretically proved that the proposed scheme is as secure as the traditional VC, while its effectiveness is validated through experiments and comparative analyses.

Currently, deep-learning-based methods have been the most popular super-resolution techniques owing to the improvement of super-resolution performance. However, they are still lack perceptual fine details and thus result in unsatisfying visual quality. This article proposes a novel method for high-quality perceptual super-resolution imaging, named SRLRGAN-SN. It aims to recovery visually plausible images with perceptual texture details by using the least squares relativistic generative adversarial network (GAN). The method applies the spectral normalization on the network with the target of enhancing the performance of GAN for super-resolution task. The least squares relativistic discriminator is designed to drive reconstruction images approximating high-quality perceptual manifold. Besides, a novel perceptual loss assembly is proposed to preserve structural texture details as much as possible. Results of experiment show that our method can not only recovery more visually realistic details, but also outperforms other popular methods regarding to quantitative metrics and perceptual evaluations.