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Reliable assessment of video quality is important for video service providers as it directly impacts a viewer's quality of experience (QoE). Proliferation of video-based applications necessitates accurate and realistic video quality assessment. Impact on a user's QoE can be estimated by evaluating the perceptual quality of a video. In this direction, two no-reference video quality assessment models are proposed in this paper. The first one is feature extraction based predicted video quality measure and and the second one is dimension adaptation based predicted video quality measure. These two predict the quality scores of a distorted video respectively before and after adaptation of its encoding dimensions. The models compute the initial frame level quality scores using a set of spatio-temporal distortions and content characteristics extracted from the video. Afterwards, a multistage temporal pooling mechanism transforms the frame level quality scores of a video into video level quality scores. Finally, a trained neuro-fuzzy model predicts a video quality score for the video. The performance of each of the models is evaluated while predicting quality of videos in publicly available video quality databases. It is observed that the predicted quality scores have good correlation with the corresponding subjective quality scores. [ABSTRACT FROM AUTHOR]

In this paper, a simple and efficient no-reference image quality assessment technique based on polynomial coefficient and reduced Gerschgorin circle is presented. The dominant features and dynamics of the test images are captured using the polynomial coefficient. Moreover, the image focus measure is modeled using the concept of reduced Gerschgorin's higher circle bound. The presented image focus measure is tested on different synthetic images datasets such as LIVE, TID2008, CSIQ, Cornel and IVC. Further, the proposed approach is also examined on real-time images captured by the camera in different conditions. It is observed that the proposed approach demonstrates important properties of focus measures such as unimodality and contrast invariance. Moreover, the presented technique is robust under the different varying noisy conditions. [ABSTRACT FROM AUTHOR]

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This paper presents a dataset comprising 700 video sequences encoded in the two most popular video formats (codecs) of today, H.264 and H.265 (HEVC). Six reference sequences were encoded under different quality profiles, including several bitrates and resolutions, and were affected by various packet loss rates. Subsequently, the image quality of encoded video sequences was assessed by subjective, as well as objective, evaluation. Therefore, the enclosed spreadsheet contains results of both assessment approaches in a form of MOS (Mean Opinion Score) delivered by the absolute category ranking (ACR) procedure, SSIM (Structural Similarity Index Measure) and VMAF (Video Multimethod Assessment Fusion). All assessments are available for each test sequence. This allows a comprehensive evaluation of coding efficiency under different test scenarios without the necessity of real observers or a secure laboratory environment, as recommended by the ITU (International Telecommunication Union). As there is currently no standardized mapping function between the results of subjective and objective methods, this dataset can also be used to design and verify experimental machine learning algorithms that contribute to solving the relevant research issues.

In this paper, a simple and efficient no-reference image quality assessment metric is proposed. It is based on the concept of polynomial coefficients, power of companion matrix, and Gerschgorin circles bound. The polynomial coefficient-based companion matrix captures the main features and dynamics of an image. Moreover, the Gerschgorin circles bound is used to define the focus metric. The proposed focus metric is tested on various real as well as synthetic image data sets. It is observed that the presented metric is unimodal to noise and invariant to the contrast changes that occur due to the variation in illumination effect. Moreover, it is robust under the varying salt-and-pepper and Gaussian noise. The performance of the proposed focus metric is also compared with the various existing focus metrics. [ABSTRACT FROM AUTHOR]

Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack of publicly available underwater image datasets with human subjective scores and reliable objective UIQA metrics. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference (NR) UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sources, covering six common underwater scenes (i.e., bluish scene, blue-green scene, greenish scene, hazy scene, low-light scene, and turbid scene), and their corresponding 900 quality improved versions are generated by employing 15 state-of-the-art underwater image enhancement and restoration algorithms. Mean opinion scores with 52 observers for each image of UID2021 are also obtained by using the pairwise comparison sorting method. Both in-air and underwater-specific NR IQA algorithms are tested on our constructed dataset to fairly compare their performance and analyze their strengths and weaknesses. Our proposed UID2021 dataset enables ones to evaluate NR UIQA algorithms comprehensively and paves the way for further research on UIQA. The dataset is available at https://github.com/Hou-Guojia/UID2021. [ABSTRACT FROM AUTHOR]

As a fundamental research problem, blind image quality assessment (BIQA) has attracted increasing interest in recent years. Although great progress has been made, BIQA still remains a challenge. To better understand the research progress and challenges in this field, we review BIQA methods in this paper. First, we introduce the BIQA problem definition and related methods. Second, we provide a detailed review of the existing BIQA methods in terms of representative hand-crafted features, learning-based features and quality regressors for two-stage methods, as well as one-stage DNN models with various architectures. Moreover, we also present and analyze the performance of competing BIQA methods on six public IQA datasets. Finally, we conclude our paper with possible future research directions based on a performance analysis of the BIQA methods. This review will provide valuable references for researchers interested in the BIQA problem. [ABSTRACT FROM AUTHOR]

In order to solve the problem of poor consistency between the traditional super-resolution image quality assessment (SRIQA) index and human subjective perception, this paper presents a reference free super-resolution image quality evaluation method by using multi-layer perceptron (MLP). This method used the pre trained VGG16 network to extract the perceptual quality features of the super-resolution image, and established the regression model between the perceptual quality features of the super-resolution image and the corresponding subjective quality score through MLP. Experimental results show that the Pearson linear correlation coefficient (PLCC) and Spearman rank order correlation coefficient (SROCC) of the proposed algorithm on the public super-resolution image data set exceed 0.95, which is significantly superior to other existing image quality evaluation methods, and has higher consistency with human subjective perception.

• We proposed a deep-learning-based model for perceptual speech quality assessment. • We presented an application of this model as perceptual regularization for speech enhancement. • Experimental results show significant improvement in the enhanced speech naturalness. Many speech enhancement methods require perceptual quality metrics for evaluation. The "holy grail" of perceptual speech quality assessment is human subjective ratings, known as the mean opinion score. However, acquiring human ratings is time-consuming, laborious, and expensive. Existing objective quality metrics, on the other hand, are efficient and easy to compute but do not correlate well with human ratings. In this paper, we propose a relatively lightweight deep-learning-based model to predict the human ratings of speech signals. Since it is differentiable, it can be easily employed as a perceptual regularization to improve existing deep-learning-based speech enhancement methods. Experimental results demonstrate that the predictions of our proposed model correlate well with human judgments. We present application in speech enhancement and show that, interestingly, while there is a degradation in performance in terms of traditional objective metrics, there is a significant improvement in the perceptual quality and the naturalness of the enhanced speech. [ABSTRACT FROM AUTHOR]

The paper presents different no reference (NR) objective metrics addressing the most important artefacts for raw (source) video sequences (noise, blur, exposure) and those introduced by compression (blocking, flickering) which can be used for assessing quality of experience. The validity of all metrics was verified under subjective tests.

Nowadays, video streaming has become one of the most dominant services due to increasing interest in watching online television programs and video-on-demand activities. Providing this service requires high quality, high speed and high-capacity network infrastructures. In this work, we propose a data-driven-based fuzzy logic inference algorithm for quality of experience (QoE) modeling of video streaming services in Addis Ababa City LTE network. The proposed fuzzy logic inference system model is used to measure the user perception from the key quality of indicator (KQI) parameters. The model is essential to replace conventional subjective measurement techniques that are costly and inefficient. In addition, the proposed fuzzy logic inference system model is helpful for business decision making, network planning and optimization activities. To analyze the performance of the proposed model, we consider main KQI parameters such as video streaming start success rate, video streaming start delay, video streaming play disconnection rate, video streaming stall frequency and video streaming stalled time rate. We perform numerical simulation to analyze the proposed model and to validate the impacts of main KQI parameters on the quality of experience in LTE video streaming services. The simulation results show that video streaming stall frequency and video streaming start delay rate play a major impact on user perception by 33% and 25%, respectively. Besides, validation of the results shows that the proposed fuzzy logic inference system model is accurate, consistent and linear compared to currently existing models.

In order to solve the problem of poor consistency between the traditional super-resolution image quality assessment (SRIQA） index and human subjective perception, this paper presents a reference free super-resolution image quality evaluation method by using multi-layer perceptron(MLP). This method used the pre trained VGG16 network to extract the perceptual quality features of the super-resolution image, and established the regression model between the perceptual quality features of the super-resolution image and the corresponding subjective quality score through MLP. Experimental results show that the Pearson linear correlation coefficient (PLCC) and Spearman rank order correlation coefficient (SROCC) of the proposed algorithm on the public super-resolution image data set exceed 0.95, which is significantly superior to other existing image quality evaluation methods, and has higher consistency with human subjective perception. [ABSTRACT FROM AUTHOR]

The present paper focuses on the contrast enhancement of an image using linear regression-based recursive sub-histogram equalization. The histogram of an image is partitioned into two non-overlapping sub-histograms using the mean intensity of the image. A set of points is constructed for each sub-histogram, considering gray level (intensity) as the abscissa and its corresponding count as the ordinate of the point. Then the method of least squares is used for fitting lines of regression for these sets of points in each sub-histogram. With the help of the regression line and histogram, intervals are created in each segmented partition. This process of creating intervals gives more intervals as compared to the Recursive Sub-Image Histogram Equalization (RSIHE) and the Mean and Variance-based Sub Image Histogram Equalization methods (MVSIHE). For qualitative and quantitative analysis of the proposed method, the experiments are performed on a set of test images, including medical and non-medical images. The evaluated results are presented in terms of various evaluation metrics. For medical images, the mean opinion score is also evaluated with the proposed method and other recent methods. The comparison with state-of-the-art methods shows the efficacy of the proposed method for enhancement. [ABSTRACT FROM AUTHOR]

As a fundamental research problem, blind image quality assessment (BIQA) has attracted increasing interest in recent years. Although great progress has been made, BIQA still remains a challenge. To better understand the research progress and challenges in this field, we review BIQA methods in this paper. First, we introduce the BIQA problem definition and related methods. Second, we provide a detailed review of the existing BIQA methods in terms of representative hand-crafted features, learning-based features and quality regressors for two-stage methods, as well as one-stage DNN models with various architectures. Moreover, we also present and analyze the performance of competing BIQA methods on six public IQA datasets. Finally, we conclude our paper with possible future research directions based on a performance analysis of the BIQA methods. This review will provide valuable references for researchers interested in the BIQA problem.

In mobile networks, handover mechanisms provide fast and smooth access service for mobile users. However, one of the main challenges in mobile networks is the handover management with increased mobility and bandwidth demand of the required network services. Therefore, in this paper, we propose a MOS-aware (mean opinion score-aware) mobile network handover mechanism based on deep learning to determine the appropriate handover time for real-time video conference services in mobile networks. We construct a wireless network topology with LTE characteristics in a Mininet-WiFi simulation. User equipment (UE) can determine the service-required MOS (Mean Opinion Score) from the proposed deep-learning-based handover mechanism with appropriate handover time. Simulation results show that the proposed scheme provides higher performance than the original A3 handover mechanism. The contribution of this paper is to combine the real-time video conferencing services with a deep-learning-based handover mechanism by predicting MOS values to improve the quality of service for users in mobile networks. [ABSTRACT FROM AUTHOR]

This paper presents a rotation-invariant and computationally efficient no-reference image quality assessment (NR-IQA) model. It estimates the image quality based on texture and structural information associated with the images. The human visual system (HVS) uses perceptual features such as texture and structure as primary information to understand the visual scene and the image content. Moreover, the texture and structural information capture the loss of naturalness due to distortions in the image. Therefore, in this work, the important texture features are extracted using the local binary patterns (LBP).The modified LBP, also known as the hyper-smoothing LBP (H-LBP) and Laplacian of H-LBP (LH-LBP), represents the image structure. Further, the image quality prediction model computes the quality of the image based on the statistical feature measures of the texture and structural information. In the proposed approach, the image quality prediction model uses support vector regression (SVR) to measure image quality. Various experimentations are carried out on the LIVE and TID2013 database to test the effectiveness of the proposed NR-IQA model. The performance metrics such as Spearman rank-ordered correlation coefficient, Pearson linear correlation coefficient, and root mean square error is computed to show the efficiency of the presented approach. The experimental results illustrate a high correlation between the predicted quality score and the human visual perceptions. It is also found to be competitive with the best-performing full-reference and no-reference IQA models.

Speech quality is often measured via subjective testing, or with objective estimators of mean opinion score (MOS) such as ViSQOL or POLQA. Typical MOS-estimation frameworks use signal level features but do not use language features that have been shown to have an effect on opinion scores. If there is a conditional dependence between score and language given these signal features, introducing language and rater predictors should provide a marginal improvement in predictions. The proposed method uses Bayesian models that predict the individual opinion score instead of MOS. Several models that test various combinations of predictors were used, including predictors that capture signal features, such as frequency band similarity, as well as features that are related to the listener, such as a language and rater index. The models are fit to the ITU-T P. Supplement 23 dataset, and posterior samples are drawn from distributions of both the model parameters and the resulting opinion score outcomes. These models are compared to MOS models by integrating over posterior samples per utterance. An experiment was conducted by ablating different predictors for several types of Bayesian hierarchical models (including ordered logistic and truncated normal individual score distributions, as well as MOS distributions) to find the marginal improvement of language and rater. The models that included language and/or rater obtained significantly lower errors (0.601 versus 0.684 root-mean-square error (RMSE)) and higher correlation. Additionally, individual rater models matched or exceeded the performance of MOS models.

Image quality markedly affects the evaluation of images, and its control is crucial in studies using natural visual scenes as stimuli. Various image elements, such as sharpness or naturalness, can impact how observers view images and, more directly, how they evaluate their quality. To gain a better understanding of the types of interactions between these various elements, we conducted a study with a large set of images with multiple overlapping distortions, covering a wide range of quality variation. Observers assigned a quality rating of the images on a 0-10 scale and gave a verbal description explaining the elements on which their rating was based. A regression model predicting image quality ratings using 68 attributes uncovered the link between verbal descriptions and quality ratings and the importance of the image quality rating for each of the 68 image attributes. Brightness, naturalness, and good colors seem to be related to the highest image quality preference. However, the most important elements for predicting good image quality were related to image fidelity such as graininess and sharpness. This indicates that a certain level of image fidelity must be achieved before more subjective associations with, for instance, naturalness can emerge. Of the attributes, 72% had a negative impact on the preference judgment. This negative bias may be due to the fact that there are more ways that observers can perceive an image to fail than to excel when they are asked to evaluate image quality. [ABSTRACT FROM AUTHOR]

Nowadays, video streaming has become one of the most dominant services due to increasing interest in watching online television programs and video-on-demand activities. Providing this service requires high quality, high speed and high-capacity network infrastructures. In this work, we propose a data-drivenbased fuzzy logic inference algorithm for quality of experience (QoE) modeling of video streaming services in Addis Ababa City LTE network. The proposed fuzzy logic inference system model is used to measure the user perception from the key quality of indicator (KQI) parameters. The model is essential to replace conventional subjective measurement techniques that are costly and inefficient. In addition, the proposed fuzzy logic inference system model is helpful for business decision making, network planning and optimization activities. To analyze the performance of the proposed model, we consider main KQI parameters such as video streaming start success rate, video streaming start delay, video streaming play disconnection rate, video streaming stall frequency and video streaming stalled time rate. We perform numerical simulation to analyze the proposed model and to validate the impacts of main KQI parameters on the quality of experience in LTE video streaming services. The simulation results show that video streaming stall frequency and video streaming start delay rate play a major impact on user perception by 33% and 25%, respectively. Besides, validation of the results shows that the proposed fuzzy logic inference system model is accurate, consistent and linear compared to currently existing models. [ABSTRACT FROM AUTHOR]

The non-orthogonal multiple access (NOMA) technology has been considered as a promising technology for the upcoming six-generation mobile communication networks (6G). However, with the increasing complexity of this access technology, how to allocate the limited network resources effectively for massive number of connectivity becomes more and more challenging. The QoE optimization problem including both the sub-channel (SC) assignment and power allocation is formulated. In order to cope with it, we first decouple it into two sub-problems: the UE-BS association, SC assignment sub-problem and the power allocation sub-problem. We thus propose genetic algorithm (GA) for UE-BS association, SC assignment and artificial bee colony (ABC) algorithm for power allocation. The corresponding constraints satisfying mechanisms are also proposed to ensure the feasibility of individual bee colony in each iteration, thus to accelerate the convergence. The simulation results show that the proposed power allocation scheme can converge to the optimal solution quickly, and the MOS can be increased by increasing the number of users (UE), and SCs. [ABSTRACT FROM AUTHOR]

Conducting user studies to quantify the Quality of Experience (QoE) of watching the increasingly more popular 360° videos in Head-Mounted Displays (HMDs) is time-consuming, tedious, and expensive. Deriving QoE models, however, is very challenging because of the diverse viewing behaviors and complex QoE features and factors. In this article, we compile a wide spectrum of QoE features and factors that may contribute to the overall QoE.We design and conduct a user study to build a dataset of the overall QoE, QoE features, and QoE factors. Using the dataset, we derive the QoE models for both the Mean Opinion Score (MOS) and Individual Score (IS), where MOS captures the aggregated QoE across all subjects, while IS captures the QoE of individual subjects. Our derived overall QoE models achieve 0.98 and 0.91 in Pearson's Linear Correlation Coefficient (PLCC) for MOS and IS, respectively. Besides, we make several new observations on our user study results, such as (1) content factors dominate the overall QoE across all factor categories, (2) Video Multi-Method Assessment Fusion (VMAF) is the dominating factor among content factors, and (3) the perceived cybersickness is affected by human factors more among others. Our proposed user study design is useful for QoE modeling (specifically) and subjective evaluations (in general) of emerging 360° tiled video streaming to HMDs. [ABSTRACT FROM AUTHOR]

Objective: Assessing the respiratory and lung mechanics of the patients in intensive care units is of utmost need in order to guide the management of ventilation support. The esophageal pressure (Peso) signal is a minimally invasive measure, which portrays the mechanics of the lung and the pattern of breathing. Because of the close proximity of the lung to the beating heart inside the thoracic cavity, the Peso signals always get contaminated with that of the oscillatory-pressure-signal of the heart, which is known as the cardiogenic oscillation (CGO) signal. However, the area of research addressing the removal of CGO from Peso signal is still lagging behind. Methods and results: This paper presents a singular spectrum analysis-based high-efficient, adaptive and robust technique for the removal of CGO from Peso signal utilizing the inherent periodicity and morphological property of the Peso signal. The performance of the proposed technique is tested on Peso signals collected from the patients admitted to the intensive care unit, cadavers, and also on synthetic Peso signals. The efficiency of the proposed technique in removing CGO from the Peso signal is quantified through both qualitative and quantitative measures, and the mean opinion scores of the denoised Peso signal fall under the categories `very good' as per the subjective measure. Conclusion and clinical impact: The proposed technique: (1) does not follow any predefined mathematical model and hence, it is data-driven, (2) is adaptive to the sampling rate, and (3) can be adapted for denoising other biomedical signals which exhibit periodic or quasi-periodic nature.

In this paper, a new no-reference image quality assessment (NR-IQA) metric is proposed. This method is based on the probability coefficients and modified entropy. The probability coefficients are used to capture the significant features and represent the important dynamics of an image. Moreover, it is used to quantify the possible loss of naturalness in a distorted image. The proposed approach has been tested on synthetic images with blurring effects and under noisy conditions. It is observed that the proposed focus measure is invariant to contrast and noisy conditions. The effectuality of the proposed approach is tested on various experimentations carried out on real-time images. The performance of proposed focus measure is better as compared to the existing image focus metrics. It also shows a good consistency and a better response under blurring effects and noises at different levels. Moreover, it exhibits a good working range, unimodality and monotonicity properties and hence satisfying the conditions of a good focus measure. Further, the various experiments on image datasets such as LIVE, TID2008 and CSIQ have shown the effectiveness of proposed NR-IQA metric. It is also observed that the presented approach has good correlation with the human opinion score as compared to the existing methods. [ABSTRACT FROM AUTHOR]

The release of WaveNet and Tacotron has forever transformed the speech synthesis landscape. Thanks to these game-changing innovations, the quality of synthetic speech has reached unprecedented levels. However, to measure this leap in quality, an overwhelming majority of studies still rely on the Absolute Category Rating (ACR) protocol and compare systems using its output; the Mean Opinion Score (MOS). This protocol is not without controversy, and as the current state-of-the-art synthesis systems now produce outputs remarkably close to human speech, it is now vital to determine how reliable this score is. To do so, we conducted a series of four experiments replicating and following the 2013 edition of the Blizzard Challenge. With these experiments, we asked four questions about the MOS: How stable is the MOS of a system across time? How do the scores of lower quality systems influence the MOS of higher quality systems? How does the introduction of modern technologies influence the scores of past systems? How does the MOS of modern technologies evolve in isolation? The results of our experiments are manyfold. Firstly, we verify the superiority of modern technologies in comparison to historical synthesis. Then, we show that despite its origin as an absolute category rating, MOS is a relative score. While minimal variations are observed during the replication of the 2013-EH2 task, these variations can still lead to different conclusions for the intermediate systems. Our experiments also illustrate the sensitivity of MOS to the presence/absence of lower and higher anchors. Overall, our experiments suggest that we may have reached the end of a cul-de-sac by only evaluating the overall quality with MOS. We must embark on a new road and develop different evaluation protocols better suited to the analysis of modern speech synthesis technologies. [ABSTRACT FROM AUTHOR]

Forecasting weather and forewarning the public with weather alerts help save lives and property immensely. Audio transmissions for alerting are not prone to local infrastructure failure and hence the most dependable. Reception of such alerts should have good clarity of audio. The Mean Opinion Score (MOS) method has traditionally been used in telephony to monitor audio reception quality. It requires an objective skilled listener using a scoring system for feedback used in improving audio quality. However, in weather radio, there is no dedicated listener 24/7. Without listening skills and listener training the MOS scoring could be skewed and of little use. Our paper focuses on the design, building and testing of audio-quality forensics system (AQFS) that uses measurements of network, radio and equipment parameters to monitor audio quality instead of listener feedback. Transmitter stations today have multiple connectivity options and the AQFS can alert the transmission operators to switch to alternate technology during failures. The AQFS also has control features to perform this switching automatically that we have tested. In this paper, we also report our findings, test results and inferences with recommendations. Apart from providing basic but detailed audio quality analysis and comparison of base and transmitted audio using two different mechanisms, the AQFS helped us detect complete audio loss, intermittent audio outages, Specific Area Message Encoding (SAME) Headers, audio anomalies, fades, drops and strange tones which correlate with fading and outage events. The analysis helped at problem transmitter sites in improving audio quality.

In mobile networks, handover mechanisms provide fast and smooth access service for mobile users. However, one of the main challenges in mobile networks is the handover management with increased mobility and bandwidth demand of the required network services. Therefore, in this paper, we propose a MOS-aware (mean opinion score-aware) mobile network handover mechanism based on deep learning to determine the appropriate handover time for real-time video conference services in mobile networks. We construct a wireless network topology with LTE characteristics in a Mininet-WiFi simulation. User equipment (UE) can determine the service-required MOS (Mean Opinion Score) from the proposed deep-learning-based handover mechanism with appropriate handover time. Simulation results show that the proposed scheme provides higher performance than the original A3 handover mechanism. The contribution of this paper is to combine the real-time video conferencing services with a deep-learning-based handover mechanism by predicting MOS values to improve the quality of service for users in mobile networks.

In a recent scenario of advancement in the upcoming next-generation system reconstructed speech(voice) signal by the side of the receiver side found stiffened, barely audible, and slim because of limited bandwidth of 300-3.4 kHz. To get back the genuineness of speech(voice) signal, narrow band speech encoders must be upgraded to wide band encoders supporting 50-kHz bandwidth. The extensive period has been left for advancement from N.B. to fully W.B.'s well-suited systems. The terminal and network must be altered to make the N.B. system compatible with the W.B. system. During that span novel technique has been urbanized to widen the N.B. bandwidth of speech (voice) signal at handset end (receiver) for humanizing final speech quality. The technique of attaining the original W.B. signal from a band-limited N.B. speech(voice) signal without actually transmitting the W.B. signal is called bandwidth extension. The same concept applies to attain super wideband from the W.B. signal. B.W.E. based on sinusoidal transform coding, linear prediction, non-linear device is giving good results compared to spectral folding/spectral translation approaches employed by various researchers. In the modern scenario of advancement in technology, various coding algorithms have been urbanized for S.W.B. and F.B. to obtain the full benefit of advancement in available telecommunications bandwidth, predominantly for the internet. In the proposed method based on source filter model fundamental thought adopted for the B. W.E. are the separate extension of the spectral envelope and the residual signal. Each part is processed separately through different speech enrichment procedure to get the highband component and added to the resampled and delayed version of the signal to acquire the final extended output which is compared through intelligibly(subjective) and quality(objective) perspective and results are compared with baseline algorithm and next-generation super wide band coder algorithms to prove that obtained results are comparable with both algorithms. Algebraic evaluation for getting missing high band components from the original W.B. signal is not needed and method tremendously well-organized and commence only minor time interval or delay.. [ABSTRACT FROM AUTHOR]

A number of full reference and reduced reference methods have been proposed in order to estimate the perceived visual quality of 3D meshes. However, in most practical situations, there is a limited access to the information related to the reference and the distortion type. For these reasons, the development of a no-reference mesh visual quality (MVQ) approach is a critical issue, and more emphasis needs to be devoted to blind methods. In this work, we propose a no-reference convolutional neural network (CNN) framework to estimate the perceived visual quality of 3D meshes. The method is called SCNN-BMQA (3D visual saliency and CNN for blind mesh quality assessment). The main contribution is the usage of a CNN and 3D visual saliency to estimate the perceived visual quality of distorted meshes. To do so, the CNN architecture is fed by small patches selected carefully according to their level of saliency. First, the visual saliency of the 3D mesh is computed. Afterward, we render 2D projections from the 3D mesh and its corresponding 3D saliency map. Then the obtained views are split into 2D small patches that pass through a saliency filter in order to select the most relevant patches. Finally, a CNN is used for the feature learning and the quality score estimation. Extensive experiments are conducted on four prominent MVQ assessment databases, including several tests to study the effect of the CNN parameters, the effect of visual saliency and comparison with existing methods. Results show that the trained CNN achieves good rates in terms of correlation with human judgment and outperforms the most effective state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Traditionally, random sensing matrices such as Gaussian are widely used in Compressed Sensing (CS). The higher computational complexity and non-structure of matrix make it inappropriate for practical use. Hence, we have projected the application of structural sensing matrices in CS. Since these matrices impose structure and hence easy to implement in hardware. In this paper, we have proposed optimization method based on structural sensing matrices and Grassmannian frame theory. These proposed sensing matrices are DCT–Grassmannian sensing matrix, Hadamard–Grassmannian sensing matrix, and Walsh–Hadamard Grassmannian sensing matrix, respectively. These sensing matrices are optimized for minimal mutual coherence based on shrinkage method; which further improves the signal recovery performance of speech signal. The result demonstrates that the performance of the proposed optimization method based on structural sensing matrices is improved compared to conventional Elad's method which employs the Gaussian sensing matrix. Furthermore, the result reveals that the proposed optimization method with Hadamard–Grassmannian sensing matrix yields better performance compared to conventional Elad's method. Finally, speech quality is evaluated using subjective MOS and objective measures such as LLR and WSS. The result exhibited that the proposed optimization method based on Hadamard–Grassmannian sensing matrix and DCT–Grassmannian sensing matrix exhibits the better MOS, LLR, and WSS performance indicating the good quality of speech. [ABSTRACT FROM AUTHOR]

In this study, the authors present results of a call testing campaign using voice over long term evolution (VoLTE) technology on the radio access network of the operator Telecom Italia mobile. The quality of the voice call is expressed by means of the mean opinion score (MOS). The electromagnetic propagation environment has been reproduced within a reverberation chamber, and its effect on MOS has been evaluated by varying the signal strength, i.e. the reference signal received power and the signal‐to‐interference noise ratio. The aim is to find a correlation between the radio propagation environment and the perceived quality of the speech. Regardless of the networks technology, voice quality could be impacted by several factors: traffic load of LTE evolved Node B, quality of service, radio pathloss, mutual radio interference, multipath and so on. Interference and multipath are added to the useful signal in order to deteriorate the MOS, until the VoLTE calls dropped. Target of such work is to properly set the thresholds that trigger a handover of single radio voice call continuity to other radio access technologies (UMTS or GSM). Tests take also into account the adaptive multi‐rate CoDec selected by the network. [ABSTRACT FROM AUTHOR]

The present paper focuses on the enhancement of magnetic resonance imaging (MRI) images of the brain tumor using the Gr ü nwald Letnikov (G-L) fractional differential mask. The method aims to enhance the edges and texture while preserving the smooth regions of an image. This will help the doctors to take a right decision for treatment by correctly identifying the location of the tumor present in an image. The method uses the G-L definition of the fractional derivative to form masks of size 3 × 3 and 5 × 5 in which the correlation of the neighboring pixels is preserved. A gradient is used to find the threshold so that the input image can be partitioned into edge, texture and smooth region. The order of the fractional derivative is chosen individually for each pixel of these three regions and the framed mask is applied on the input image to get the enhanced image. To show the effectiveness of the proposed method, results are presented in terms of visual appearance, subjective assessment and quantitative metrics. PSNR, AMBE, entropy, and GLCM are used as evaluation parameters for quantitative analysis. The comparison with other existing methods such as fixed order fractional differential, adaptive fractional differential, and modified G-L differential operator shows the improvement in results obtained by the proposed method. [ABSTRACT FROM AUTHOR]

This work was motivated by previous studies on the perceptual influence of chromatic impairments, where was observed that specific chromatic impairments might have a different influence, related to the represented content. Hence, in this study, chromatic impairments were applied to specific color clusters and the resulting perceptual influence was analyzed. The applied chromatic impairments influence on the images naturalness was assessed using a Single Stimulus Continuous Quality Evaluation (SSCQE) and the Mean Opinion Score (MOS) was calculated. Furthermore, during the image assessment, the eye movements were registered using an eye tracking device and the Relative Fixation Time (RFT) was computed. It was concluded that the induced chromatic impairments, lead to lower MOS values than the original images, revealing a perception of quality loss. Furthermore, when nature colors are changed, subjects revealed a larger perception of the impairment, resulting in smaller MOS and producing evident changes in the RFT's. The comparative analysis between the MOS and the RFT's reveals high Pearson Correlation Coefficient (PCC) results (p = 0.79 ± 0.14). [ABSTRACT FROM AUTHOR]

One problem about using video calls or video telephony applications is a quality issue. Therefore, this paper contributes the simple QoE model, a mathematical model, to assess quality of video calls provided by video telephony applications or services. This mathematical model has been created based on the results from 200 subjects who joined the subjective test using short conversation scenario via two applications, Skype and LINE, two popular applications/services in Thailand. Based on the subjective results, the weighted coefficients for audio quality and video quality have been found, while the coefficient for audio quality is weighted higher than the one for video quality. Nevertheless, after using ANOVA and t-test, statistical analysis tools, based on the first set of the subjective data, it has been found that there are significant differences (p-values < 0.05) when compared to most previous QoE models from other research works. However, after performance evaluating using Mean Absolute Percent Error (MAPE), it has been found that its performances are better than previous models with average MAPE values of 10.4 and 5.65% in the first and the second round evaluation with the other 48 subjects and 32 subjects respectively. Therefore, these are evident that this model can be applied to evaluation quality of video calls with confidence. [ABSTRACT FROM AUTHOR]

This research proposes an enhanced measurement method for VoIP quality assessment which provides an improvement to accuracy and reliability. To improve the objective measurement tool called the simplified E-model for the selected codec, G.729, it has been enhanced by utilizing a model of Mean Opinion Score (MOS), called a subjective MOS prediction model, based on native Thai users who use the Thai-tonal language. Then, the different results from the simplified E-model and subjective MOS prediction model were used to create the Bias function, before adding to the simplified E-model. Finally, it has been found that the outputs from the enhanced simplified E-model for the G.729 codec show better accuracy when compared to the original simplified E-model, specially, after the enhanced model has been evaluated with 4 test sets. The major contribution of this enhancement is that errors are reduced by 58.87% when compared to the generic simplified E-model. That means the enhanced simplified E-model as proposed in this study can provide improvement beyond the original simplified one significantly. [ABSTRACT FROM AUTHOR]

Many histogram equalisations‐based methods have been proposed to be applied in contrast enhancement. However, a few methods that can simultaneously create a natural enhancement in images with low, median, and high brightness ranges are suggested. Here, a robust contrast enhancement algorithm, which is called triple clipped dynamic histogram equalisation based on standard deviation (TCDHE‐SD), is proposed. In the proposed method, the histogram of the input image is partitioned into three parts with approximately the same number of pixels based on the standard deviation. Then, the process of histogram clipping is performed on each sub‐histogram. After that, all histograms will be mapped to a new dynamic range by means of applying some simple calculations, and finally, the sub‐histogram equalisation process will be performed independently. This method is proposed to achieve the multiple purposes of the maximum average information content (entropy), controlling the enhancement ratio and preserving reasonable brightness. It also provides a natural enhancement by generating clear images with maximum details. The performance assessment of the proposed method in terms of entropy as well as visual quality based on the mean opinion score (MOS) indicates a significant advantage of it over the previous ones. [ABSTRACT FROM AUTHOR]

In web applications, for efficient use of bandwidth and storage requirement, images are compressed with lossy techniques. Quality of images degrades due to the compression artifacts. Transmission channels also contribute in degradation by adding noise. Image quality assessment plays a vital role to address this issue. In majority of cases, reference image remains unavailable for the assessment. Thus, No-Reference quality assessment techniques are widely used. In this paper, machine learning based hybrid approach for NR quality assessment is proposed. Blockiness based parameters, other statistical parameters and NSS based features are provided as input to the feed forward neural network. The back propagation training algorithm predicts a quality score. This score is correlated with differential mean opinion score (DMOS). Here, input can be images of any type from best to worst quality, and the approach used exploits the nonlinearity in the behaviors of parameters. It has been noticed that the predicted score correlates well with the DMOS with 93% accuracy. The above parameters are also used as input to Support Vector Machine for classification. The observed accuracy of this classifier is 89%. [ABSTRACT FROM AUTHOR]

In data service applications over the Internet, user perception and satisfaction can be assessed by quality of experience (QoE) metrics. QoE depends both on the users' perception and the used service, which together form end-to-end metrics. While network optimization has traditionally focused on optimizing network properties such as QoS, we focus in this work on optimizing end-to-end QoE metrics with the aim to deliver to the client a good QoE that can be monitored in real time. We argue that end-user QoE is a relevant measurement for network operators and service providers. In this paper, we present a machine learning approach combined with adaptive video delivery service in order to provide a better QoE for video streaming services. This solution will be established using SDN architecture. The first part of the paper deals with a brief introduction of SDN networks, QoE requirement, and ML algorithms. Secondly, we expose the rating of the web application that we developed. This will help in conducting a subjective study to collect MOS on real-time as well as objective parameters SSIM, VQM, and PSNR. At the end, we expose our QoE-aware monitoring approach and explain what it is based on. [ABSTRACT FROM AUTHOR]

Spectrum handoff plays an important role in cognitive radio networks (CRNs). Secondary users (SUs) use spectrum handoff to hold on the idle channel or to free the channel for primary users (PUs). Spectrum handoff scheme greatly affects the transmission quality and the success rate of SUs connection. In this Letter, a reinforcement learning‐based spectrum handoff scheme with the measured packet drop rate (PDR) for multimedia transmissions over CRNs is proposed. In a system model with multiple PUs and SUs, a new state space description method is designed and an observed state includes not only the status whether PUs arrive on each channel but also several other important factors. Also, the measured PDR, instead of the calculated one, is presented to update the mean opinion score, the Q‐table and the handoff policy. Compared with the existing schemes with the calculated PDR from the Quality‐of‐Experience model, the authors' proposed scheme can converge more rapidly in the dynamic radio environment, and reduce the PDR of SUs more significantly. [ABSTRACT FROM AUTHOR]

Various visual distortions can inevitably affect the 3D meshes during their transmission and geometrical processing. In most practical cases, blind quality assessment becomes a challenging issue due to the unavailability of reference meshes and distortion related information. In this paper, we present a novel method to blindly assess the quality of 3D meshes. This method relies on a feature learning based approach to predict the objective quality scores. For this, we propose the mesh dihedral angles statistics as a feature and the support vector regression (SVR) as a learning tool based quality predictor. The proposed method takes into account the main functions of the human visual system (HVS) by introducing the visual masking and the saturation effects. Experiments have been successfully conducted on LIRIS/EPFL general-purpose, LIRIS Masking and UWB compression databases. The obtained results show that the proposed method provides good correlation and competitive scores comparing to some influential and effective full and reduced reference existing methods. [ABSTRACT FROM AUTHOR]

One of the main goals of the future wireless networks is improving the users’ quality of experience (QoE). In this paper, we consider the problem of the QoE-based resource allocation in the downlink of a massive multiple-input multiple-output heterogeneous network. The network consists of a macrocell with a number of small cells embedded in it. The small cells’ base stations (BSs) are equipped with a few antennas, while the macro BS is equipped with a massive number of antennas. We consider the two services Video and Web Browsing and design the beamforming vectors at the BSs. The objective is to maximize the aggregated mean opinion score (MOS) of the users under constraints on the BSs’ powers and the required quality of service of the users. We also consider extra constraints on the QoE of users to more strongly enforce the QoE in the beamforming design. To reduce the complexity of the optimization problem, we suggest suboptimal and computationally efficient solutions. Our results illustrate that increasing the number of antennas at the BSs and also increasing the number of small cells’ antennas in the network leads to a higher user satisfaction. [ABSTRACT FROM AUTHOR]

A large number of subjective image quality assessment databases have been constructed in the last decade, in which the Mean Opinion Score (MOS) (with single or double stimulus), and Paired Comparison (PC) are two dominant approaches for collecting the ground truth quality ratings and usually up to 15 or more subjects are needed for each image. In this paper, we show the fact that there is a potential “dictatorship” risk of using such averaging-over-multiple-rating type of method. Using Arrow’s Impossibility Theorem (AIT), we prove that meeting of the unanimity and independence of irrelevant alternatives (IIA) will generate a “pivotal subject”, who in fact determines the final rank of image quality. We also prove that no an ideal democratic approach to synthesize the whole opinions of subjects. Therefore, we advocate to recruit a small number of experts (a.k.a the “golden eyes”) for subjective viewing tests. In order to verify the reliability of our proposal, experiments on two different databases conducting on the general distorted images and professional images (here is Terahertz security image) are performed. In each experiment, the raw scores of images are subjectively assigned by at least 15 inexperienced viewers and 3 experts, and meanwhile the MOS or difference mean opinion score (DMOS) are obtained. Afterwards, the correlation of the scores rated by naive subjects and experts is analyzed. For general image experiment, it is revealed that DMOS of inexperience viewers are highly related to DMOS of experts based on six effective evaluation metrics. In professional image experiment, the preferences of experts also maintain favourable relevance with the opinions of inexperienced viewers in overall quality of THz image. Moreover, considering the quality assessments of illegal substance regions in THz images, the experts have higher accuracy than the inexperienced observers. In conclusion, the results of two validation experiments verify that a small number of experts are more suitable for assessing the perceptual quality of images, which can reduce cost and simplify procedure of creating databases. [ABSTRACT FROM AUTHOR]

Advancements in electronics and miniaturized device fabrication technologies have enabled simultaneous acquisition of multiple biosignals (MBioSigs), but the area of compression of MBioSigs remains unexplored to date. This paper presents a robust singular value decomposition (SVD) and American standard code for information interchange (ASCII) character encoding-based algorithm for compression of MBioSigs for the first time to the best of our knowledge. At the preprocessing stage, MBioSigs are denoised, down sampled and then transformed to a two-dimensional (2-D) data array. SVD of the 2-D array is carried out and the dimensionality of the singular values is reduced. The resulting matrix is then compressed by a lossless ASCII character encoding-based technique. The proposed compression algorithm can be used in a variety of modes such as lossless, with or without using the down sampling operation. The compressed file is then uploaded to a hypertext preprocessor (PHP)-based website for remote monitoring application. Evaluation results show that the proposed algorithm provides a good compression performance; in particular, the mean opinion score of the reconstructed signal falls under the category “very good” as per the gold standard subjective measure. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a Multi-Constraint Quality-of-Experience (QoE) centric Routing (MCQR) technique for efficient real-time video streaming over Mobile Ad-hoc Networks (MANETs). The dynamic estimation of link quality metric during MCQR route discovery process is the key of enhancing QoE at end user. MCQR limits end-to-end delay and jitter of received data packets by selecting long lifetime routes with maximum permissible delay. This minimizes the distortion caused fluctuations in perceived video. To prove the effectiveness of MCQR for real-time video streaming, we have designed a hybrid MANET consisting of simulated nodes and physical machines. In our hybrid network, the source and destination nodes are video traffic generators and receivers running on physical devices. Emulation results are obtained using real-time video streaming over high fidelity virtual wireless network, and conforming that the MCQR protocol provides satisfactory QoE to end users as compared to existing similar techniques. The packet delivery ratio of MCQR is higher than 90 % and its mean opinion score is higher than three (i.e., satisfactory) even when mobility and shadowing effects are kept high in target network scenarios. These improvements are achieved along with still maintaining similar or better routing overhead and processing delays. [ABSTRACT FROM AUTHOR]

Referenceless image quality assessment is a challenging and critical problem in today's multimedia applications. Texture patterns in images are normally at high frequencies compared to lower ones. Due to the effect of distortions during acquisition, compression, and transmission, texture deviation artifacts are generated that cause a granular effect in the image. Other artifacts, such as blocking, affect high frequencies in an image, causing distorted edges. Combining the analysis of texture deviation and other artifacts helps in determining the quality of an image. The proposed approach uses variation in the energy of pixels to quantify the quality of an image. These variations are calculated using texture energy measures and pattern strength-based statistical features. In the proposed approach, machine learning-based classifiers are used to predict the quality score for an image. The performance of the proposed method is tested for all images ranging from pristine to poor quality from LIVE and TID2008 databases. For different distortions, results are shown to have good correlation if they lie between the predicted score and the differential mean opinion score. Results obtained with this approach are compared with other widely used referenceless approaches. It is observed that the proposed approach shows better performance in the quantification of the quality of an image. [ABSTRACT FROM AUTHOR]

A non‐intrusive speech quality assessment method for complex environments was proposed. In the proposed approach, a new sparse representation‐based speech reconstruction algorithm was presented to acquire the quasi‐clean speech from the noisy degraded signal. Firstly, an over‐complete dictionary of the clean speech power spectrum was learned by the K‐singular value decomposition algorithm. Then in the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross‐correlation and the noise spectrum which was adjusted by a posteriori SNR‐weighted factor, and the orthogonal matching pursuit approach was applied to reconstruct the clean speech spectrum from the noisy speech. The quasi‐clean speech was considered as the reference to a modified PESQ perceptual model, and the mean opinion score of the noisy degraded speech was achieved via the distortions estimation between the quasi‐clean speech and the degraded speech. Experimental results show that the proposed approach obtains a correlation coefficient of 0.925 on NOIZEUS complex environment database, which is 99% similar to the performance of the intrusive standard ITU‐T PESQ, and 7.1% outperforms non‐intrusive standard ITU‐T P.563. [ABSTRACT FROM AUTHOR]

In this study, an efficient hidden Markov model (HMM)‐based bilingual speech synthesiser for the Indian language Tamil and Indian English is developed. Initially, phone mapping approach is tried to synthesise English text using Tamil corpus alone by mapping English phonemes to the perceptually similar phonemes in Tamil, and is found that the approach is language‐dependent and requires a large dictionary for Indian pronunciation. Therefore, given the speech data for both languages, the straight‐forward approach to develop a bilingual synthesiser is to build separate synthesiser for each language and combine them or by merging the perceptually similar phonemes. These approaches introduce language‐switching/influence in the synthesised speech. To minimise switching and influence, HMM‐based bilingual synthesisers are developed by merging acoustically similar phonemes, derived based on model parameters and likelihood Gaussian using various distance metrics. The performance of these synthesisers are evaluated based on mean opinion score (MOS), language‐switching and language‐influence. Results reveal that the set of phonemes derived using product‐of‐likelihood Gaussians in the likelihood space is the optimum set of phonemes that can be merged and the system developed by merging these phonemes outperforms the rest with an MOS of 3.66. Furthermore, only 8% and 23% of the sentences have language‐switching/influence, respectively. [ABSTRACT FROM AUTHOR]

This article reports on a multi-lab subjective listening experiment aiming at inter-lab and intra-lab test results repeatability verification. An identical set of speech samples corresponding to contemporary networks has been tested by three independent labs deploying ITU-T P.835 methodology. The tests results have been compared regarding Pearson correlation, RMSE, RMSE* and numbers of opposite pair-wise comparisons. The results show the level of inter-lab and intra-lab repeatability in the case of identical test speech samples utilization and thus confirm the subjective tests are highly repeatable in case they follow recommendation requirements strictly. The tests also show differences in results in case subject expectations are set differently using a wider set of test speech samples (as presented in one of the labs). [ABSTRACT FROM AUTHOR]

With the explosive growth of mobile video applications, analysis of video quality becomes increasingly important because it is an important Key Performance Indicator (KPI) for Quality of Experience (QoE). In this paper, a framework for non-reference video quality analysis is proposed and applied to Video Telephony (VT) in LTE networks. Three metrics, blockiness, blur and freezing, are used to estimate the MOS. Blockiness is detected by taking the H.264 codec features into account, blur is estimated by utilizing the percentage of noticeable blurred edges in each frame, and freezing is evaluated by using a sigmoid function to mimic the effect of different freezing duration on the Human Visual System (HVS). Furthermore, the three metrics are combined into one objective MOS by considering different weighting factors and using the linear curve fitting. Above 90% correlation is achieved between the objective MOS score and subjective MOS score. [ABSTRACT FROM AUTHOR]