Search

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]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

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., (© 2024 The Author(s).)

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]