In this paper, we present the effectiveness of image compression based on a convolutional auto encoder (CAE) with region of interest (ROI) for quality control. We propose a method that adapts image quality for prioritized parts and non-prioritized parts for CAE-based compression. The proposed method uses annotation information for the distortion weights of the MS-SSIM-based loss function. We show experimental results using a road damage image dataset that is used to check damaged parts and an image dataset with segmentation data (ADE20K). The experimental results reveals that the proposed weighted loss function with CAE-based compression from F. Mentzer et al. learns some characteristics and preferred bit allocations of the prioritized parts by end-to-end training. In the case of using road damage image dataset, our method reduces bpp by 31% compared to the original method while meeting quality requirements that an average weighted MS-SSIM for the road damaged parts be larger than 0.97 and an average weighted MS-SSIM for the other parts be larger than 0.95.

Air quality index (AQI) is a non-dimensional index for the description of air quality, and is widely used in air quality management schemes. A novel method for Air Quality Index Forecasting based on Deep Dictionary Learning (AQIF-DDL) and machine vision is proposed in this paper. A sky image is used as the input of the method, and the output is the forecasted AQI value. The deep dictionary learning is employed to automatically extract the sky image features and achieve the AQI forecasting. The idea of learning deeper dictionary levels stemmed from the deep learning is also included to increase the forecasting accuracy and stability. The proposed AQIF-DDL is compared with other deep learning based methods, such as deep belief network, stacked autoencoder and convolutional neural network. The experimental results indicate that the proposed method leads to good performance on AQI forecasting.

Media Access Control (MAC) is critical to guarantee different Quality of Service (QoS) requirements for Unmanned Aerial Vehicle (UAV) networks, such as high reliability for safety packets and high throughput for service packets. Meanwhile, due to their ability to provide lower delay and higher data rates, more UAVs are using frequently directional antennas. However, it is challenging to support different QoS in UAV networks with directional antennas, because of the high mobility of UAV which causes serious channel resource loss. In this paper, we propose CU-MAC which is a MAC protocol for Centralized UAV networks with directional antennas. First, we design a mobility prediction based time-frame optimization scheme to provide reliable broadcast service for safety packets. Then, a traffic prediction based channel allocation scheme is proposed to guarantee the priority of video packets which are the most common service packets nowadays. Simulation results show that compared with other representative protocols, CU-MAC achieves higher reliability for safety packets and improves the throughput of service packets, especially video packets.

Power line communications (PLC) is a communication technology that uses a power-line as a transmission medium. Previous studies have shown that connecting an AC adapter such as a mobile phone charger to the power-line affects signal quality. Therefore, in this paper, the authors analyze the influence of chargers on inter-computer communications using packet capture to evaluate communications quality. The analysis results indicate the occurrence of a short duration in which packets are not detected once in a half period of the power-line supply: named communication forbidden time. For visualizing the communication forbidden time and for evaluating the communications quality of the inter-computer communications using PLC, the authors propose an instantaneous power-line frequency synchronized superimposed chart and its plotting algorithm. Further, in order to analyze accurately, the position of the communication forbidden time can be changed by altering the initial burst signal plotting position. The difference in the chart, which occurs when the plotting start position changes, is also discussed. We show analysis examples using the chart for a test bed data assumed an ideal environment, and show the effectiveness of the chart for analyzing PLC inter-computer communications.

Thanks to the ability to bring immersive experiences to users, Virtual Reality (VR) technologies have been gaining popularity in recent years. A key component in VR systems is omnidirectional content, which can provide 360-degree views of scenes. However, at a given time, only a portion of the full omnidirectional content, called viewport, is displayed corresponding to the user's current viewing direction. In this work, we first develop Weighted-Viewport PSNR (W-VPSNR), an objective quality metric for quality assessment of omnidirectional content. The proposed metric takes into account the foveation feature of the human visual system. Then, we build a subjective database consisting of 72 stimuli with spatial varying viewport quality. By using this database, an evaluation of the proposed metric and four conventional metrics is conducted. Experiment results show that the W-VPSNR metric well correlates with the mean opinion scores (MOS) and outperforms the conventional metrics. Also, it is found that the conventional metrics do not perform well for omnidirectional content.

This paper reports a high-quality hardware-friendly integer motion estimation (IME) scheme. According to different characteristics of CTU content, the proposed method adopts different adaptive multi-resolution strategies coupled with accurate full-PU modes IME at the finest level. Besides, by using motion vector derivation, IME for the second reference frame is simplified and hardware resource is saved greatly through processing element (PE) sharing. It is shown that the proposed architecture can support the real-time processing of 4K-UHD @60fps, while the BD-rate is just increased by 0.53%.

In this paper the quality of adaptive bit rate video streaming is investigated and two state-of-the-art models, i.e., the NTT audiovisual quality-estimation and ITU-T P.1203 models, are considered. This paper shows how these models can be applied to new conditions, e.g., 4K ultra high definition (4K-UHD) videos encoded using H.265, considering that they were originally designed and trained for HD videos encoded with H.264. Six subjective evaluations involving up to 192 participants and a large variety of test conditions, e.g., durations from 10sec to 3min, coding-quality variation, and stalling events, were conducted on both TV and mobile devices. Using the subjective data, this paper addresses how models and coefficients can be transferred to new conditions. A comparison between state-of-the-art models is conducted, showing the performance of transferred and retrained models. It is found that other video-quality estimation models, such as VMAF, can be used as input of the NTT and ITU-T P.1203 long-term pooling modules, allowing these other video-quality-estimation models to support the specificities of adaptive bit-rate-streaming scenarios. Finally, all retrained coefficients are detailed in this paper allowing future work to directly reuse the results of this study.

The capacity of optical transport networks has been increasing steadily and the networks are becoming more dynamic, complex, and transparent. Though it is common to use worst case assumptions for estimating the quality of transmission (QoT) in the physical layer, over provisioning results in high margin requirements. Accurate estimation on the QoT for to-be-established lightpaths is crucial for reducing provisioning margins. Machine learning (ML) is regarded as one of the most powerful methodological approaches to perform network data analysis and enable automated network self-configuration. In this paper, an artificial neural network (ANN) framework, a branch of ML, to estimate the optical signal-to-noise ratio (OSNR) of to-be-established lightpaths is proposed. It takes account of both nonlinear interference between spectrum neighboring channels and optical monitoring uncertainties. The link information vector of the lightpath is used as input and the OSNR of the lightpath is the target for output of the ANN. The nonlinear interference impact of the number of neighboring channels on the estimation accuracy is considered. Extensive simulation results show that the proposed OSNR estimation scheme can work with any RWA algorithm. High estimation accuracy of over 98% with estimation errors of less than 0.5dB can be achieved given enough training data. ANN model with R=4 neighboring channels should be used to achieve more accurate OSNR estimates. Based on the results, it is expected that the proposed ANN-based OSNR estimation for new lightpath provisioning can be a promising tool for margin reduction and low-cost operation of future optical transport networks.

Current image quality assessment (IQA) methods require the original images for evaluation. However, recently, IQA methods that use machine learning have been proposed. These methods learn the relationship between the distorted image and the image quality automatically. In this paper, we propose an IQA method based on deep learning that does not require a reference image. We show that a convolutional neural network with distortion prediction and fixed filters improves the IQA accuracy.

In conventional video streaming systems, various kind of video streams are delivered from a dedicated server (e.g., edge server) to the subscribers so that a video stream of higher quality level is encoded with a higher bitrate. In this paper, we consider the problem of delivering those video streams with the assistance of Peer-to-Peer (P2P) technology with as small server cost as possible while keeping the performance of video streaming in terms of the throughput and the latency. The basic idea of the proposed method is to divide a given video stream into several sub-streams called stripes as evenly as possible and to deliver those stripes to the subscribers through different tree-structured overlays. Such a stripe-based approach could average the load of peers, and could effectively resolve the overloading of the overlay for high quality video streams. The performance of the proposed method is evaluated numerically. The result of evaluations indicates that the proposed method significantly reduces the server cost necessary to guarantee a designated delivery hops, compared with a naive tree-based scheme.

Many quality metrics have been proposed for the compliance perception to assess haptic device performance and perceived results. Perceived compliance may be influenced by factors such as object properties, experimental conditions and human perceptual habits. In this paper, analysis of softness perception was conducted to find out relevant quality metrics dominating in the compliance perception system and their correlation with perception results, by expressing these metrics by basic physical parameters that characterizing these factors. Based on three psychophysical experiments, just noticeable differences (JNDs) for perceived softness of combination of different stiffness coefficients and damping levels rendered by haptic devices were analyzed. Interaction data during the interaction process were recorded and analyzed. Preliminary experimental results show that the discrimination ability of softness perception changes with the ratio of damping to stiffness when subjects exploring at their habitual speed. Analysis results indicate that quality metrics of Rate-hardness, Extended Rate-hardness and ratio of damping to stiffness have high correlation for perceived results. Further analysis results show that parameters that reflecting object properties (stiffness, damping), experimental conditions (force bandwidth) and human perceptual habits (initial speed, maximum force change rate) lead to the change of these quality metrics, which then bring different perceptual feeling and finally result in the change of discrimination ability. Findings in this paper may provide a better understanding of softness perception and useful guidance in improvement of haptic and teleoperation devices.

Deterioration of software quality developed by multiple organizations has become a serious problem. To predict software degradation after an organizational change, this paper investigates the influence of quality deterioration on software metrics by analyzing three software projects. To detect factors indicating a low evolvability, we focus on the relationships between the change in software metric values and refactoring tendencies. Refactoring after an organization change impacts the quality.

Speech captured by an in-ear microphone placed inside an occluded ear has a high signal-to-noise ratio; however, it has different sound characteristics compared to normal speech captured through air conduction. In this study, a method for blind speech quality enhancement is proposed that can convert speech captured by an in-ear microphone to one that resembles normal speech. The proposed method estimates an input-dependent enhancement function by using a neural network in the feature domain and enhances the captured speech via time-domain filtering. Subjective and objective evaluations confirm that the speech enhanced using our proposed method sounds more similar to normal speech than that enhanced using conventional equalizer-based methods.

This letter presents an objective quality index for benchmarking image inpainting algorithms. Under the guidance of the masks of damaged areas, the boundary region and the inpainting region are first located. Then, the statistical features are extracted from the boundary and inpainting regions respectively. For the boundary region, we utilize Weibull distribution to fit the gradient magnitude histograms of the exterior and interior regions around the boundary, and the Kullback-Leibler Divergence (KLD) is calculated to measure the boundary distortions caused by imperfect inpainting. Meanwhile, the quality of the inpainting region is measured by comparing the naturalness factors between the inpainted image and the reference image. Experimental results demonstrate that the proposed metric outperforms the relevant state-of-the-art quality metrics.

This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming. Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered. MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming. Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming. These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.

Saliency quality assessment aims at estimating the objective quality of a saliency map without access to the ground-truth. Existing works typically evaluate saliency quality by utilizing information from saliency maps to assess its compactness and closedness while ignoring the information from image content which can be used to assess the consistence and completeness of foreground. In this letter, we propose a novel multi-information fusion network to capture the information from both the saliency map and image content. The key idea is to introduce a siamese module to collect information from foreground and background, aiming to assess the consistence and completeness of foreground and the difference between foreground and background. Experiments demonstrate that by incorporating image content information, the performance of the proposed method is significantly boosted. Furthermore, we validate our method on two applications: saliency detection and segmentation. Our method is utilized to choose optimal saliency map from a set of candidate saliency maps, and the selected saliency map is feeded into an segmentation algorithm to generate a segmentation map. Experimental results verify the effectiveness of our method.

In the current era of data science, data quality has a significant and critical impact on business operations. This is no different for the meteorological data encountered in the field of meteorology. However, the conventional methods of meteorological data quality control mainly focus on error detection and null-value detection; that is, they only consider the results of the data output but ignore the quality problems that may also arise in the workflow. To rectify this issue, this paper proposes the Total Meteorological Data Quality (TMDQ) framework based on the Total Quality Management (TQM) perspective, especially considering the systematic nature of data warehousing and process focus needs. In practical applications, this paper uses the proposed framework as the basis for the development of a system to help meteorological observers improve and maintain the quality of meteorological data in a timely and efficient manner. To verify the feasibility of the proposed framework and demonstrate its capabilities and usage, it was implemented in the Tamsui Meteorological Observatory (TMO) in Taiwan. The four quality dimension indicators established through the proposed framework will help meteorological observers grasp the various characteristics of meteorological data from different aspects. The application and research limitations of the proposed framework are discussed and possible directions for future research are presented.

To recognize transportation modes without any additional sensor devices, we demonstrate that the transportation modes can be recognized from communication quality factors. In the demonstration, instead of using global positioning system (GPS) and accelerometer sensors, we collect mobile TCP throughputs, received-signal strength indicators (RSSIs), and cellular base-station IDs (Cell IDs) through in-line network measurement when the user enjoys mobile services, such as video streaming. In accuracy evaluations, we conduct two different field experiments to collect the data in six typical transportation modes (static, walking, riding a bicycle, riding a bus, riding a train and riding a subway), and then construct the classifiers by applying a support-vector machine (SVM), k-nearest neighbor (k-NN), random forest (RF), and convolutional neural network (CNN). Our results show that these transportation modes can be recognized with high accuracy by using communication quality factors as well as the use of accelerometer sensors.

Device-to-device (D2D) communication in cellular networks is defined as direct communication between two mobile users without traversing the base station (BS) or core network. D2D communication can occur on the cellular frequencies (i.e., inband) or unlicensed spectrum (i.e., outband). A high capacity IEEE 802.11-based outband device-to-device communication system for cellular networks is introduced in this paper. Transmissions in device-to-device connections are managed using our proposed medium access control (MAC) protocol. In the proposed MAC protocol, backoff window size is adjusted dynamically considering the current network status and utilizing an appropriate transmission attempt rate. We have considered both cases that the request to send/clear to send (RTS/CTS) mechanism is and is not used in our protocol design. Describing mechanisms for guaranteeing quality of service (QoS) and enhancing reliability of the system is another part of our work. Moreover, performance of the system in the presence of channel impairments is investigated analytically and through simulations. Analytical and simulation results demonstrate that our proposed system has high throughput, and it can provide different levels of QoS for its users.

In many applications, tables are distributively stored in different data sources, but the frequency of updates on each data source is different. Some techniques have been proposed to effectively express the temporal orders between different values, and the most current, i.e. up-to-date, value of a given data item can be easily picked up according to the temporal orders. However, the currency of the data items in the same table may be different. That is, when a user asks for a table D, it cannot be ensured that all the most current values of the data items in D are stored in a single table. Since different data sources may have overlaps, we can construct a conjunctive query on multiple tables to get all the required current values. In this paper, we formalize the conjunctive query as currency preserving query, and study how to generate the minimized currency preserving query to reduce the cost of visiting different data sources. First, a graph model is proposed to represent the distributed tables and their relationships. Based on the model, we prove that a currency preserving query is equivalent to a terminal tree in the graph, and give an algorithm to generate a query from a terminal tree. After that, we study the problem of finding minimized currency preserving query. The problem is proved to be NP-hard, and some heuristics strategies are provided to solve the problem. Finally, we conduct experiments on both synthetic and real data sets to verify the effectiveness and efficiency of the proposed techniques.