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Dynamic Vision Sensors (DVS) are emerging retinomorphic visual capturing devices, with great advantages over conventional vision sensors in terms of wide dynamic range, low power consumption, and high temporal resolution. The bio-inspired approach of the DVS results in lower data rates than conventional vision sensors. Still, such data can be further compressed. Compression of DVS data is an emerging research area and a detailed performance comparison of different compression strategies for these data is still missing. This paper addresses lossless compression strategies for data output by neuromorphic visual sensors. We compare the performance of a number of strategies, including the only strategy developed specifically for such data and other more generic data compression strategies, tailored here to the case of neuromorphic data. We perform the comparison in terms of compression ratio, as well as compression and decompression speed and latency. Moreover, the compression performance analysis is performed under diverse scenarios including stationary and mobile DVS. According to the detailed experimental analysis, Lempel-Ziv-Markov chain algorithm (LZMA) achieves the best compression ratios among all the considered strategies for the case when the DVS is static. On the other hand, Spike coding achieves the best compression ratios under the scenario when spike events are produced by a sensor in motion. However, both strategies result in low compression speed and high latency which restrict the applications of these strategies in real-time scenarios. The Brotli strategy achieves the best trade-off between compression ratio, speed and latency under static as well as mobile scenarios. We also observe a significant decrease in compression and decompression performance (in terms of ratio, speed and latency) of all the strategies under mobile DVS scenarios.

With the recent increased usage of video services, the focus has recently shifted from the traditional quality of service-based video delivery to quality of experience (QoE)-based video delivery. Over the past 15 years, many video quality assessment metrics have been proposed with the goal to predict the video quality as perceived by the end user. HTTP adaptive streaming (HAS) has recently gained much attention and is currently used by the majority of video streaming services, such as Netflix and YouTube. HAS, using reliable transport protocols, such as TCP, does not suffer from image artifacts due to packet losses, which are common in traditional streaming technologies. Hence, the QoE models developed for other streaming technologies alone are not sufficient. Recently, many works have focused on developing QoE models targeting HAS-based applications. Also, the recently published ITU-T Recommendation series P.1203 proposes a parametric bitstream-based model for the quality assessment of progressive download and adaptive audiovisual streaming services over a reliable transport. The main contribution of this paper is to present a comprehensive overview of recent and currently undergoing works in the field of QoE modeling for HAS. The HAS QoE models, influence factors, and subjective test methodologies are discussed, as well as existing challenges and shortcomings. The survey can serve as a guideline for researchers interested in QoE modeling for HAS and also discusses possible future work.

Rate adaptation of video can result in varying Quality of Experience (QoE) over time. For instance, the latest HTTP-based video streaming approaches rely on rate adaptation techniques to overcome the negative effects of time varying channel conditions. Therefore, in order to have a satisfactory user’s QoE in these situations, it is necessary to evaluate and predict subjective quality variations over time, termed here as Time Varying Subjective Quality (TVSQ). It is a challenge to model TVSQ due to hysteresis effects and non-linear responses of our Human Visual System (HVS). This study proposes a moving average filter based time varying quality metric, which accounts for the recency and primacy effects of our HVS. The performance of the proposed method is evaluated over a publicly available TVSQ database, which consists of 15 x 300-seconds videos evaluated by 25 subjects for time varying quality. The results show a good performance with respect to the state of the art. The simple calculations and the accuracy of the proposed method enable us to use this approach in real-time HTTP based streaming applications.

With the increasing demand in video traffic, delivering appropriate Quality of Experience may appear as a challenge for highly popular services such as Twitch.tv and YouTube-Gaming. This paper presents the evaluation of three widely used encoders, namely H.264, H.265 and AV1, for the case of gaming videos, assuming streaming scenario. The performance of the codecs has been assessed in terms of objective video quality metrics (PSNR, SSIM and VMAF) and subjective video quality assessment. The encoding settings were kept similar for all the three aforementioned encoders. In the considered setting, AV1 achieves a better level of video quality both in terms of objective and subjective VQA, for almost all bitrates and content considered. The improvement is remarkable in particular for the lower range of bitrates considered.

Neuromorphic sensors, a.k.a. dynamic vision sensors (DVS) or silicon retinas, do not capture full images (frames) at a fixed rate, but asynchronously capture spikes indicating changes of brightness in the scene, following the principles of biological vision and perception in mammals. DVS sensing and processing produces a data representation where the scene can be represented with a very high time resolution with a limited number of bits (an inherent data compression is performed at the time of acquisition). Such representation can be used locally to derive actionable responses and selected parts can be transmitted and then processed in another network location. Due to these features, such sensors represent an excellent choice as visual sensing technology for next-generation Internet-of-Things, e.g. in surveillance, drone technology, and robotics. It is in fact becoming evident that in this framework acquiring, processing, and transmitting frame-based video is inefficient in terms of energy consumption and reaction times, in particular in some scenarios. Hence, we explore here the feasibility of advanced Machine to Machine (M2M) communications systems that directly capture, compress and transmit spike-based visual information to cloud computing services in order to produce content classification or retrieval results with extremely low power and low latency.

Dynamic Vision Sensors (DVS) are emerging neuromorphic visual capturing devices, with great advantages in terms of low power consumption, wide dynamic range, and high temporal resolution in diverse applications. The capturing method results in lower data rates than conventional video. Still, such data can be further compressed. This is an emerging research area and a performance comparison of different compression strategies for these data is still missing. This paper addresses lossless compression strategies for data output by neuromorphic visual sensors. We compare the performance of a number of strategies, including the only strategy developed specifically for such data and other more generic data compression strategies, tailored here to the case of neuromorphic data. We perform the comparison in terms of compression ratio, as well as compression and decompression speed. According to the detailed experimental analysis, LZMA achieves the best compression ratio among all the considered strategies. On the other hand, Brotli achieves the best trade-off between speed (compression and decompression) and compression ratio.

Traditional multimedia contents only stimulate the senses sight and hearing, therefore the research efforts try to provide realistic media contents to the users to stimulate the other senses. Realistic media contents are media with multiple sensory effects, with the aim of enriching the user's experience through the five senses representation, which is considered as an interesting method to enhance the Quality of Experience (QoE). In the home environment, customary devices with opportune smart features (e.g., air conditioning, lights, etc.) can be employed to deliver the sensory effects. A subjective test measurement campaign has been conducted to assess the QoE for multi-sensorial media in a real smart home scenario. Based on QoE assessment results, this paper discusses the research challenges and open issues for multi-sensorial applications in smart home scenario, which may motivate additional researches in this emerging domain of study.

In wireless systems, content-aware MAC layer scheduling strategies contribute to supporting an adequate Quality of Service (QoS) and Quality of Experience (QoE) for the users. Content related information that can be used in such strategies include information about the video frame type (Intra-Predicted, Inter-Predicted or Backward-Predicted). Frame type identification and prediction in the MAC layer scheduler (as well as in other network locations) is hence a critical part of content-aware resource management and traffic engineering in video streaming. It is important to note that, when encryption is adopted at the upper layers, packet inspection is not possible. To address this issue, we propose an adaptive clustering and prediction algorithm. The algorithm uses unsupervised clustering combined with an adaptive classification approach to automatically group packets into frame clusters. Unlike conventional video traffic classifiers, our approach requires neither a-priori knowledge of video frames pattern nor a training set of frames. Instead, this approach automatically classifies packets into different frame types by employing simple statistical features. The proposed method continuously learns from the upcoming traffic thus the proper frame class labels can be easily discovered. Furthermore, the proposed method uses an autoregressive integrated moving average (ARIMA) algorithm to predict the upcoming traffic frame type. A large number of experiments has been carried out using data from several video samples. Results show the robustness and the effectiveness of our classification method, which is capable of achieving a detection rate of up to 97.3% for I frames and overall 2 identification accuracy of 91.3% (for all I, P and B frames).

Spatial Information (SI) and Temporal Information (TI) have been used widely as an approximate estimation of video complexity. Recently, SI (and TI) have found use in many other applications such as Quality of Experience modeling, Bandwidth and Rate-distortion modeling, etc., for both traditional and non-traditional (gaming, dynamic vision sensors, etc.) videos. It is often assumed that SI and TI only depend on video content, while instead factors such as resolution, bit depth, compression have an impact on the values of SI and TI for a specific video content. A systematic study on SI and TI for videos, investigating the effect of different video encoding and processing steps on SI and TI values has been missing so far. Also, SI and TI calculation has been limited to 8-bit videos, while there has been increasing popularity and usage of 10-bit videos. Towards this end, we present in this paper a comprehensive evaluation of the variation of SI and TI for different 8-bit and 10-bit videos. Results and insights into the variation of SI and TI values for different encoding settings, choice of encoders, temporal pooling methods, resolution, etc. are presented in this study.

Super multi-view light-field displays provide glasses-free immersive 3D experience via continuous motion parallax. Such displays, particularly large ones, require input views acquired at high angular resolution, covering a large field of view. Light-field content can be captured or generated by several different tools and methods, such as the turntable arrangement for a single camera, arc, linear and grid arrangements of multiple cameras, light-field cameras and also virtual cameras. However, acquiring content with a large number of real cameras is often economically and technologically prohibitive. Interpolating intermediate views from those captured using sparse camera arrays has been suggested to achieve the required high view density. Such synthesized views suffer from several artifacts, which can severely affect the perceived quality. Objective quality assessment of synthesized images has been studied extensively on a per-image basis. Yet, when such content is visualized on a light-field display, the objective quality of the content may not be derived from the quality of the individual images. In this paper, we present a pilot study where we investigated degradations in the objective quality of 2D synthesized views while increasing the proportion of synthesized views in the multi-view content.

In recent years, 3D surface imaging has been adopted in oculofacial surgeries; 3D measurements have facilitated surgical planning and post-operative assessment. Furthermore, life-like 3D visualization has provided a promising alternative as tools of communication and training. However, such surface imaging and visualization technologies remain restricted to the legacy systems. In this paper, we present a 3D surface imaging and reconstruction system made from off-the-shelf components. Our imaging setup consists of custom-made stereo cameras and structured light projection. We demonstrate a true-to-scale 3D reconstruction of the frontal part of the head of a human subject, by capturing its multiple views. We use a non-intrusive auto-calibration method to ensure reconstruction accuracy. The paper also discusses the lighting conditions of such capture procedures, as such parameters may severely affect the performance.

Gaming as a popular system has recently expanded the associated services, by stepping into live streaming services. Live gaming video streaming is not only limited to cloud gaming services, such as Geforce Now, but also include passive streaming, where the players' gameplay is streamed both live and ondemand over services such as Twitch.tv and YouTubeGaming. So far, in terms of gaming video quality assessment, typical video quality assessment methods have been used. However, their performance remains quite unsatisfactory. In this paper, we present a new No Reference (NR) gaming video quality metric called NR-GVQM with performance comparable to state-of-the-art Full Reference (FR) metrics. NR-GVQM is designed by training a Support Vector Regression (SVR) with the Gaussian kernel using nine frame-level indexes such as naturalness and blockiness as input features and Video Multimethod Assessment Fusion (VMAF) scores as the ground truth. Our results based on a publicly available dataset of gaming videos are shown to have a correlation score of 0.98 with VMAF and 0.89 with MOS scores. We further present two approaches to reduce computational complexity.

Event-based cameras are very promising sensors for high speed mobile robots, self-driving cars and drones. Event-based Vision Sensors output a sequence of events data rather than a video bitstream as for classical cameras. The data rate output by such sensors depends upon the type of scene and camera motion speed. Estimating the data rate associated to such sensors is needed for the appropriate design of transmission systems. This paper suggests considering information on the scene content to support such estimation. The considered content parameter shows a high correlation with the event rate, appearing as a promising parameter for event rate and hence data rate estimation.

High-Efficiency Video Coding (HEVC) is the latest video compression standard developed by the Joint Collaborative Team on Video Coding (JCT-VC). This standard is developed with an aim to reduce the video bitrate requirements by 50% with no degradation in the video quality. In the field of telemedicine, the HEVC standard can help in lowering the bitrate requirements for coding medical videos and thus enhance their compression efficiency and transmission performance over communication channels. In this paper, a comparative performance evaluation of the HEVC standard with its predecessor the H.264/AVC has been carried out in the context of medical videos. The evaluation results are achieved using three different configuration modes currently provided by the JCT-VC team, namely the random access, low delay and intra configurations. In order to perform a fair comparative evaluation, the H.264/AVC standard is also configured to match as close as possible to the configuration modes of the HEVC standard. The encoded videos of both HEVC and H.264 standards are transmitted over simulated 4G networks in order to study its influence on system resource utilisation and transmission efficiency. The test results show that the HEVC standard gives higher compression efficiency over the H.264/AVC standard, resulting in a reduction in bitrate requirements, which further helps in achieving better quality transmission over LTE/LTE-A networks.

Door phone systems, allowing occupants of a building to communicate with visitors at the door, have evolved over the years, with the current advancements being a fully internet protocol (IP) based solution. In order to adopt newer IP based solutions, current analogue systems can be replaced, yet this may be costly and cumbersome, especially in a conventional multi-occupant building. We therefore propose an architecture which supports current analogue door phone systems, and also provides IP based functionality. We have implemented the proposed architecture based on SIP, WebRTC and an IoT gateway system connected to the multi-occupant conventional video door phone system.

Interpolating virtual views from sparse visual content increases the angular resolution of the visualization. However, such techniques may degrade the image quality through inaccurate view estimation. The smooth motion parallax through increased angular resolution, and image quality are essential components of the overall user experience. The choice of interpolation initiates an unclear trade-off between them. In this paper, we introduce our research on interpolation techniques for 3D light field visualization. We examined the performance and perceived quality of selected algorithms, in a series of subjective quality assessment experiments. Our results show an obvious interpolation technique preference on visual contents with low angular resolutions.

There is an ongoing debate in the research community over the improved visual quality of UHD video in comparison to the still widely-deployed HD standard. It is the inspiration of many scientific studies, yet UHD displays and services are continuously spreading on the consumer market. This paper presents the results of a subjective paired-comparison test with both upscaled HD and UHD video sequences, investigating the primary research question of whether UHD can offer a significant improvement over HD for common viewing conditions. In our study, subjects rated their visual preference on video-only clips without lossy encoding. In addition we studied cognitive biases, by presenting a label of what users were about to see (HD or UHD) before the sequence, purposely manipulating the labels in some conditions to suggest different resolutions than actually shown. Finally, we investigated the impact of different rating scales on the precision of the results. Our studies show that HD clips appear indistinguishable from UHD clips when the rating scale chosen is not fine-grained enough. We also found that the labeling effect has a significant impact on the perceived quality, overriding the actual visual perception. Even with a precise scale, users cannot detect major improvements of UHD compared to HD, which may let us question the added value offered by UHD.

The smooth motion parallax of light field displays is one of the most critical enablers of the glasses-free auto-stereoscopic 3D experience. This smoothness originates from an acceptable degree of angular resolution, which is derived from the number of views visualized in the given field of view. However, if human observers view the content without any movement, the requirement for angular resolution can be lower. In this paper, we introduce the results of a subjective quality assessment of visual content displayed on a light field cinema. The angular resolution of the content was different in each test condition, and participants were located in fixed positions during the experiment.

We present a new approach for image and video quality evaluation in telemedicine applications. Our approach relies on analyzing the quality of a pre-known reduced size logo embedded in an unused part of the medical ultrasound frame. The method is tested using two different objective metrics, namely: the Peak Signal to Noise Ratio (PSNR) and the Structural SIMilarity index metric (SSIM). We show that the presented method, not needing the original frame to predict the quality, achieves a high correlation coefficient (more than 0.9) for the different quality metrics used. We also present relationships between the quality derived via the logo and via the original frame and we assess the overhead in data transmission resulting from the compressed logo data and its protection overhead.

We present a survey of psychophysiology-based assessment for quality of experience (QoE) in advanced multimedia technologies. We provide a classification of methods relevant to QoE and describe related psychological processes, experimental design considerations, and signal analysis techniques. We summarize multimodal techniques and discuss several important aspects of psychophysiology-based QoE assessment, including the synergies with psychophysical assessment and the need for standardized experimental design. This survey is not considered to be exhaustive but serves as a guideline for those interested to further explore this emerging field of research.

Angular resolution plays a vital role in the perceived quality when displaying visual content on autostereoscopic 3D displays, since it affects the motion parallax effect. In this paper, we present the results of a subjective quality assessment carried out on a light field display, investigating the perceptual quality of visual content with different angular resolutions. We also address the question of subjective assessment validity, since the visual experience of suboptimal, reduced angular resolution is currently a completely new phenomenon for test participants.

User experience of 3D visualization on light field displays depends on several technical and human factors. One of these factors is Region of Interest, which determines the properties of visualization, such as the level of zoom. In this paper we present the results of three experiments investigating the effect of level of zoom on Quality of Experience. These experiments use different subjective quality assessment methods to evaluate user experience. Beyond the subjective preference of the visualized 3D content, visual comfort is measured as well.

This work focuses on Quality of Experience (QoE) based scheduling of multiple video flows on the downlink of LTE/LTE-A system. The LTE/LTE-A network architecture is not built for QoE-based video delivery. LTE/LTE-A enables Quality of Service (QoS) based traffic prioritization. QoS allows network operators to split traffic into flows based on attributes such as traffic type (streaming video, real-time video or VoIP) and traffic needs (packet delay, jitter or throughput). In this work, we consider scalable video layers mapped to QoE-based priority classes at the core network. At the Radio Access Network (RAN), packets of different priority classes receive different forwarding treatment (scheduling and queue management). In the recent approaches, the QoE marked packets are dropped by utilizing a packet dropping algorithm, and scheduled by employing a QoS-aware scheduler. In this work, we proposed a QoE-based priority class aware scheduling approach which, under congestion, does not require a packet dropping algorithm. Furthermore, the proposed approach not only conforms to the LTE/LTE-A hierarchical architecture but also delivers to the end users a better QoE as delivered by the state-of-the-art cross-layer approaches.

In recent years, HTTP video streaming has become a dominant technology for multimedia content consumption. Frame freezing occurring in the video stream is considered one of the key factors affecting the perceptual visual quality at the client side. In this paper we provide an analysis of how frame freezing in 3D video streams is observed over time, addressing the question whether the user starts tolerating freezes or gets irritated by them. The amount of Temporal Information of the video sequences is taken into consideration, since significant differences between adjacent frames can increase the detectability of freezing.

The interruptions of playback fluency are considered to be essential phenomena in the degradation of video quality at the user's side during the consumption of HTTP streaming services. Indeed, rebuffering can certainly negatively affect the overall experience, however, the extent of annoyance depends on the content and also on the parameters of stalling, like frequency or duration. In this paper we present a subjective video quality assessment measurement in which a series of videos containing different distributions of frame freezing were evaluated by human observers. The selected points of rebuffering were chosen based on their Temporal Information, which can reflect motion content.

The paper describes the features of the centrifugal balance developed at INRIM, which is a suitable weighing instrument for accurate mass determination of small object in weightless conditions. A new measurement model is proposed, which allows to measure any kinds of objects, solid and not with an accuracy comparable to a classic laboratory balance.

The quality of visual contents displayed on 3D autostereoscopic displays – such as light field displays – essentially depend on factors that are not present in case of 3D stereoscopic or 2D ones, like angular resolution. A higher number of views in a given field of view enables a smoother, continuous motion parallax, but evidently requires more resources to transmit and display. However, in several cases a sufficiently high number of views might not even be available, thus light field reconstruction is required to increase the density of intermediate views. In this paper we introduce the results of a research aiming to measure the perceptual difference between light field reconstruction and different angular resolutions via a series of subjective image quality assessments. The analysis also calls attention to transmission requirements of content for light field displays.

The interruption of video playback continuity, also identified from users' perspective as rebuffering or stalling, is one of the key phenomena that can hinder user experience during streaming video services. Its subjective observation, however, may change over time, similarly to most human factors. In this paper we present the results of a research designed to investigate how the perception of rebuffering during video streams varies over time, and how it correlates with the perceptual abilities of individuals. The subjective tests were performed on an autostereoscopic, glasses-free 3D display, as our experiment also studies the depth distance and thus the perceived size of objects in video content with a given motion during stalling events.

In a prior work, we derived the LLRTD metric for frame synchronization over a binary symmetric channel (BSC), where data frames are of unknown and unequal lengths and with synchronization words aperiodically inserted, data symbols have a known, unequal distribution. The performance of the metric was evaluated using simulations. Here we provide an analytical performance evaluation for the LLRTD metric. For the purpose of comparison, we also provide an analytical performance evaluation for the hard correlation (HC) metric in the case where the data has a known, unequal distribution. The results obtained for both metrics are found to be in line with the simulation results earlier obtained and confirm that the LLRTD outperforms the HC metric in all cases considered.

Reducing the queue length and queuing delay in the buffer which in turn reduces congestion and latency in the network are of great importance for real-time video services. In this paper, we propose a perceptual quality-aware queue management mechanism to shorten the queue length and waiting time of video traffic in a finite buffer. Our proposed approach prioritizes video layers in a stream in accordance with their hierarchical structure and selectively drops packets from different layers under a target user perceived quality constraint. This in turn reduces network load and latency and increases the capacity to serve more concurrent streams while satisfying users' perceptual quality requirements. We develop a statistical queuing model to express the delay requirements of video layers with an equivalent cross-layer constraint We then formulate the queue management problem as a minimization of queuing delay using a selective packet drop approach under perceptual quality and queue expiry time provisioning. Numerical results show that our proposed approach outperforms the widely deployed random early detection (RED) and tail drop (ID) queue management schemes in terms of reducing queuing delay and queue length in the buffer, while increasing the fraction of perceived quality-satisfied users.

A stochastic radio resource allocation (RRA) algorithm is designed to maximize the total transmission rate of orthogonal frequency-division multiple access (OFDMA) cognitive radios (CRs) with hybrid (i.e., joint underlay and overlay) spectrum access strategy. Our novel solution incorporates the probabilities of channel availability obtained through spectrum sensing for allocating power and subcarrier in a multi-user multi-band environment. In order to protect the licensed users from harmful intervention under imperfect sensing information, stochastic transmit and interference power constraints are imposed on the CRs. The performance of the proposed RRA algorithm and advantages over the conventional hard-decision-based approaches are demonstrated via simulation.

This paper proposes an approach for the evaluation of DASH-based video transmission from a server located in a Content Delivery Network (CDN) to multiple LTE users. The approach is based on utilizing an analytical model for the HTTP/TCP transmission in the wired/core link, and simulation traces for the packet transmission in the wireless/access link. The core and access link are separately considered by using the Wireless Transmission Control Protocol (WTCP) at the eNodeB, and three scheduling approaches are used for delivering the video packets in the access link. Therefore, in our proposed approach, we emulate DASH-based end-to-end video delivery and observe how the scheduling strategy affects the video streaming quality depending on the number of users in the system.

We propose an optimal radio resource allocation (RRA) scheme for scalable H.264/SVC multi-user video transmission over downlink orthogonal frequency division multiple Access (OFDMA)-based cognitive radio (CR) networks. Our framework adopts a new probabilistic approach to mitigate the total imposed interference by cognitive users on the licensed spectrum. We consider two fundamental network service objectives, i.e., the number of satisfied users and the video quality. We devise the 3-dimensional scalable quality of the H.264/SVC video transmission for an OFDMA-based CR network and develop efficient suboptimal algorithms to solve the probabilistic constrained mixed discrete-continuous non-linear programming (MDCNLP) problem. Simulation results indicate that the proposed quality-aware scheme can achieve up to 1.3 dB increase in the average peak signal-to-noise ratio (PSNR) per user over the conventional non-quality-aware RRA algorithms.

This work focuses on scheduling multiple video flows on the downlink of a wireless system based on Orthogonal Frequency Division Multiple Access (OFDMA), such as the LTE/LTE-A standard. We propose a joint multi-user scheduling and multi-user rate adaptation strategy by considering Scalable Video Coding (SVC) which facilitates the truncation of bit streams, thus allowing graceful degradation of video quality in the event of wireless channel variations or network congestion. The proposed scheduler utilizes Quality of Experience (QoE)-aware priority marking, where video layers are mapped to priority classes, and targets at minimizing delay bound violations for the most important priority classes under congestion. In order to reduce congestion, we propose multi-user rate adaptation at the MAC layer via a novel dynamic filtering policy for QoE based priority classes. Simulation results show that the proposed approach delivers to the end users a better QoE, with lower complexity, than state-of-the-art cross-layer approaches.

Diverse scheduling strategies have been designed for video streaming traffic ranging from Quality of Service (QoS) aware scheduling rules to more complex video quality based scheduling strategies. In this work, we analyze and compare some of the well known scheduling rules for video streaming traffic. Our main goal is to compare and analyze different classes of scheduling strategies (QoS and video quality aware rules) in terms of network centric performance metrics as well as user centric metrics. QoS evaluation involves the evaluation of network performance parameters, e.g., packet loss rate, average system throughput and end-to-end packet delay. On the other hand, video quality evaluation involves the computation of objective and subjective video quality metrics. According to simulation results, the proxy based video quality aware scheduling strategy performs best in terms of number of satisfied users and should be used in an Long-Term Evolution (LTE) downlink to offer high quality video streaming services.

Recent advancements in 3D imaging, coding, compression, storage, transmission, and error concealment techniques enable wide usage of 3D video/image applications. Video quality assessment plays a major role in improving the perceived quality at the receiver side, since such information can be used at the transmitter or in the different network nodes for system optimization ‘on-the-fly’. Most of the objective quality metrics in use are Full-Reference (FR), requiring the original video sequance for comparison. In the case of quality assessment for stereoscopic video, both left and right views need to be considered. In this paper, we introduce a novel Reduced-Reference (RR) quality metric for stereoscopic video using 3D structural tensors, based on the fact that the Human Visual System (HVS) is more sensitive to the structural information present in the scene. This method incorporates a new saliency detection method by considering spatial and temporal aspects of the video sequance. The Correlation Coefficient (CC) calculated for the obtained results shows that the values of the derived metric are well correlated with the corresponding subjective test results.

The problem of frame synchronization with equiprobable data symbols is widely analyzed in the literature and the optimum metric for AWGN channels has been derived both in the case of periodically and aperiodically embedded data symbols. On the contrary, the case of non-equiprobable data symbols has not been studied, although it can occur in many practical situations. An optimum metric for frame synchronization in data streams with non-uniformly distributed data symbols is derived in this paper and its performance is investigated through simulation. A performance comparison with the metric which is optimal for equiprobable data symbols is also provided. Results show that the derived optimum metric results in an evident gain, at the expense of a small additional complexity.

Enabling 3D video in e-health applications results in the provision of more natural viewing conditions and improved diagnosis. Recent advances in wireless communications, network technologies and 3D video capture/display mechanisms enable the replacement of 2D video applications of current healthcare services with 3D video, to provide improved perception and diagnosis. Remote surgery and surgery training can benefit in particular from 3D video due to the added dimension of depth. This paper envisages a wireless health system which transmits 3D surgical video over next generation wireless networks. Asymmetric coding of 3D video to provide effective medical services over band limited wireless networks is investigated in this paper. The quality of the compressed 3D medical video with the proposed method is measured both objectively and subjectively for a range of compression levels.

This paper presents an analysis of medical video streaming based on the Dynamic Adaptive Streaming over HTTP (DASH) standard from the Moving Picture Experts Group (MPEG).We propose two adaptation approaches at the client and evaluate parameters of the Quality of Experience (QoE), i.e., the quality perceived by the users, by investigating the number and duration of re-buffering events, the added time in the playout, and the Peak Signal-to-Noise Ratio (PSNR) of the medical video frames. The results show that the selection of MPEG-DASH related parameters and the adaptation approach at the client result in improved quality of the medical video streaming. In the end, we discuss how the elaborated approaches and results could be utilized to facilitate and enhance telemedicine applications.

Healthcare services are increasingly using medical images and video in their applications. Quality assessment can be performed via image quality evaluation metrics. However, due to the specific nature of the associated data, quality evaluation of medical images poses several issues. In this paper, we provide an insight into medical image quality evaluation, by discussing the issues faced and the current trends in the literature. Based on the analysis of the medical and technical literature, three broad categories of quality evaluation metrics are presented. The different methodologies are compared in different environments and services and recommendations for future research are provided.

We consider in this paper downlink scheduling at the MAC layer for delay sensitive traffic in wireless systems based on Orthogonal Frequency Division Multiple Access (OFDMA), such as the recent LTE/LTE-A wireless standard. In the literature, time diversity is exploited in scheduling by using the Head of Line (HoL) packet delay and time-averaged bit throughput. However, under diverse channel conditions and variable rate characteristics of video traffic, the average throughput along with the HoL packet delay does not provide a fair system. We advocate here that, instead of utilizing the average throughput, the utilization of time-averaged channel quality along with the HoL packet delay and Packet Loss Rate (PLR) can reduce the Quality of Service (QoS) violations of low channel quality users and thus provide a fair system.

This paper proposes a novel segment-based teletraffic model for dynamic adaptive video streaming over HTTP (DASH), based on the MPEG-DASH standard. The functionality of the standard's framework is mapped on the proposed model for which the probabilities of buffer overflow, empty buffer and active buffer are adopted as relevant performance metrics. These metrics describe the MPEG-DASH streaming process from the teletraffic viewpoint and are observed for different segment sizes of the encoded video at the server and different buffer sizes at the client. The results show that small segment sizes of the encoded video increase the probabilities of empty buffer and active buffer, while decreasing the probability of buffer overflow, whereas large segment sizes are suitable for decreasing the probabilities of empty buffer and active buffer, while increasing the probability of buffer overflow. Regardless of the segment sizes, the performance metrics are further improved when the buffer size at the client is increased.

We consider in this paper downlink scheduling at the MAC layer for delay sensitive traffic in wireless systems based on Orthogonal Frequency Division Multiple Access (OFDMA). We propose a downlink scheduling strategy for scalable video transmission to multiple users over OFDMA systems, such as the recent LTE/LTE-A wireless standard. We use a payoff metric based on video frame importance in conjunction with our proposed scheduler. The priority function on each Physical Resource Block (PRB), considering video frame importance and bit throughput, efficiently exploits multi-user diversity as compared to state-of-the-art strategies. Simulation results confirm the efficient utilization of time and frequency diversity by showing an improvement in the objective video quality of each user as compared to state-of-the-art strategies.

In this paper, we propose a strategy for resource allocation for different traffic classes at the Medium Access Control (MAC) layer of wireless systems based on Orthogonal Frequency Division Multiple Access (OFDMA), such as the recent Long-Term Evolution (LTE) wireless standard. In order to achieve inter-class fairness, we propose a modification of the Virtual Token Modified Largest Weighted Delay First (VT-M-LWDF) and Modified Largest Weighted Delay First (M-LWDF) rules. Through simulation, we show that the proposed scheduler introduces remarkable multi-objective improvement of the Quality of Service (QoS) performance parameters, i.e., Packet Loss Rate (PLR), average throughput, fairness index and system spectral efficiency, among different classes of traffic such as video, VoIP and best-effort.

The latest advances on robotic surgery enable the performance of many surgical procedures by utilizing minimally invasive techniques. In particular, recent 3-D endoscopes have improved the performance of minimally invasive surgical procedures. Based on these advances, performing or visualizing in real-time surgical procedures at a distance can be envisaged. In this paper, we present a performance evaluation of 3-D robotic tele-surgery and training over next generation wireless networks, namely wireless networks based on the long term evolution (LTE) 3GPP standard. Different scheduling strategies are compared and results are analyzed in term of the resulting quality of experience (QoE) for the surgeon.

3D video quality evaluation received much attention from researchers due to the demand from the industry and to the complex nature of true 3D video perception. The availability of public 3D video databases provides an opportunity for researchers and developers to evaluate novel objective 3D video quality evaluation metrics. In this paper, a stereoscopic 3D video database for the evaluation of visual quality assessment metrics is described. The effects of random packet losses on the overall 3D perception (i.e., distortions due to different packet loss rates) are considered in this research. The database presented here contains 54 test stimuli from 9 reference test video sequences corresponding to 6 different packet loss rates, including the uncorrupted 3D video sequence. In order to obtain true 3D video perception, about 1730 individual human quality observations (Opinion Scores (OS) of subjects) are considered for this database. The obtained Differential Mean Opinion Scores (DMOS) can be effectively used for evaluating 3D video quality metrics, as well as for designing new 3D video quality evaluation methods. Together with DMOS values, we provide the corresponding objective quality measurements using several objective quality metrics. The designed 3D video database will be freely available for download and use in scientific research.

Based on hypothesis testing and using the likelihood ratio test (LRT), optimum frame synchronization metrics have been recently proposed for data transmission over an additive white Gaussian noise (AWGN) channel for frames which are of unknown, variable lengths. The derived metrics addressed the cases where data symbols have an equiprobable distribution, where the distribution of the data symbols is unknown, and where data symbols have a known, unequal distribution. In this paper, following a similar approach, a frame synchronization metric is proposed for transmission over a binary symmetric channel (BSC), where frames are of unknown, variable lengths and with data symbols which have a known, unequal distribution. The performance of this metric is compared via simulation with the commonly used hard correlation (HC) metric. The obtained receiver operating characteristic (ROC) curves show that an evident performance improvement can be obtained with the use of the proposed metric.

The recent Long-Term Evolution (LTE) standard, thanks to the provision of high data rates, will enable future immersive and interactive multimedia applications over wireless. In this paper, we study the performance of 2D and 3D video transmission over LTE networks. The LTE standard bandwidth ranges are considered in order to explore the impact of the LTE bandwidth scalability on the transmission of 2D and 3D video traffic to the end users. This dependency is investigated through the packet loss ratio (PLR) and average throughput as user-oriented metrics, and the cell spectral efficiency as a system-oriented metric. Furthermore, a PLR-based Admission Control (AC) strategy is introduced in the system for which the achieved trade-off between the system resource utilization and the quality level provided to the different users is investigated. The simulation results provide guidelines for combining bandwidth scalability and admission control strategies in LTE networks in order to achieve high system resource utilization and video quality for the LTE users.

This paper presents the PIDIM architecture in order to assist in the customization of user interfaces for mobile devices in pervasive environments. This architecture uses concepts of Pervasive Computing and represents data on the user's profile, so that adaptation of the interfaces is entirely focused on the end user. The knowledge representation about the user profile is done through ontologies due to the possibility of reuse of stored information. In order to validate and demonstrate the flow of operation of the proposed approach is presented a case study, which has as scenario the adaptation of user interfaces when it is in motion.