Showing papers in "IEEE Transactions on Broadcasting in 2023"

Why No Reference Metrics for Image and Video Quality Lack Accuracy and Reproducibility

Abstract: This article provides a comprehensive overview of no reference (NR) metrics for image quality analysis (IQA) and video quality analysis (VQA). We examine 26 independent evaluations of NR metrics (previously published) and analyze 32 NR metrics on six IQA datasets and six VQA datasets (new results). Where NR metric developers claim Pearson correlation values between 0.66 and 0.99, our measurements range from 0.0 to 0.63. None of the NR metrics we analyzed are accurate enough to be deployed by industry. Performance evaluations that indicate otherwise are based on insufficient data and highly inaccurate. We will examine development strategies, tools, datasets, root cause analysis, and our baseline metric for collaboration, Sawatch.

3-D Hybrid Beamforming for Terahertz Broadband Communication System With Beam Squint

Abstract: In this paper, the three-dimensioned (3D) hybrid beamforming is designed for the terahertz (THz) based broadband broadcasting communication system with beam squint. Specifically, the full-dimensional THz massive multiple input multiple output (M-MIMO) channel model and the array gain of the uniform planar array (UPA) are first analyzed in the context of the beam squint effect. Then, a 3D hybrid beamforming architecture is proposed by leveraging two-tier true time delay (TTD), which is able to combat the beam squint effect from the horizontal and vertical directions. To further reduce hardware cost and power consumption, a low-cost 3D hybrid beamforming architecture with a two-tier TTD and phase shifter combination (TPC) is designed. Simulation results are shown that the performance of the proposed 3D hybrid beamforming architectures is capable of approaching that of the full-digital beamforming counterpart in the face of beam squint, despite relying on reduced implementation cost.

An End-to-End Blind Image Quality Assessment Method Using a Recurrent Network and Self-Attention

Abstract: In this paper, we propose a blind image quality assessment (BIQA) method using self-attention and a recurrent neural network (RNN); this approach can effectively capture both local and global information from an input image. The implementation of our constructed deep no-reference (NR) assessment framework does not rely on any convolutional operations. First, the capture step for obtaining locally significant information is performed by a self-attention operation inside a divided window. Second, we design a serialized feature input memory subnetwork to fuse the global features of the image. Finally, all the integrated features are uniformly mapped to the target score. The experimental results obtained on publicly available benchmark IQA databases show that our approach outperforms other state-of-the-art algorithms.

Why No Reference Metrics for Image and Video Quality Lack Accuracy and Reproducibility

Abstract: This article provides a comprehensive overview of no reference (NR) metrics for image quality analysis (IQA) and video quality analysis (VQA). We examine 26 independent evaluations of NR metrics (previously published) and analyze 32 NR metrics on six IQA datasets and six VQA datasets (new results). Where NR metric developers claim Pearson correlation values between 0.66 and 0.99, our measurements range from 0.0 to 0.63. None of the NR metrics we analyzed are accurate enough to be deployed by industry. Performance evaluations that indicate otherwise are based on insufficient data and highly inaccurate. We will examine development strategies, tools, datasets, root cause analysis, and our baseline metric for collaboration, Sawatch.

Attentional Feature Fusion for End-to-End Blind Image Quality Assessment

Abstract: In this paper, an end-to-end blind image quality assessment (BIQA) model based on feature fusion with an attention mechanism is proposed. We extracted the multilayer features of the image and fused them based on the attention mechanism; the fused features are then mapped into score, and the image quality assessment without reference is realized. First, because the human visual perception system hierarchically approaches the input information from local to global, we used three different neural networks to extract physically meaningful image features, and we use modified VGG19 and modified VGG16 to extract the substrate texture information and the local information of the edges, respectively. Meanwhile, we use the resNet50 to extract high-level global semantic information. Second, to take full advantage of multilevel features and avoid monotonic addition in hierarchical feature fusion, we adopt an attention-based feature fusion mechanism that combines the global and local contexts of the features and assigns different weights to the features to be fused, so that the model can perceive richer types of distortion. Experimental findings on six standard databases show that our approach yields improved performance.

Joint Visible Light Sensing and Communication Using m-CAP Modulation

Abstract: As 5G devices and networks continue to roll out, new broadcasting services and capabilities have been introduced to the entire ecosystem, opening up additional new applications and granular business opportunities, where indoor joint communication and sensing are critical. Under this trend, in this paper, we propose a new system using multi-band carrierless amplitude and phase $(m$ -CAP) modulation associated with received signal strength (RSS)-based trilateration to achieve visible light sensing and communication from the same signal. The architecture of this system is first detailed, with an emphasis on how the light source limitations in terms of dynamic range and modulation bandwidth may be taken into account. The proposed set-up is then shown through simulations to provide an illuminance between 300 and 500 lux over the whole room, positioning with an error lower than 7.17 cm in 90% of the cases, and a continuous data connectivity at 32 Mbps. The influence of several parameters, including that of the main $m$ -CAP settings, on this performance is studied in order to define some general rules for the design of such a system.

Implementation of Wireless Backhaul and Inter-Tower Communications With MIMO in ATSC 3.0

Abstract: Wireless backhaul, i.e., in-band distribution links (IDL) and inter-tower communications networks (ITCN) were previously proposed as key enabling technologies for the next-generation digital broadcasting systems, e.g., the Advanced Television Systems Committee (ATSC) 3.0 system. Both ITCN/IDL can be operated in the spectrum-efficient in-band full-duplex (IBFD) mode and the same frequency band is shared between the broadcast service signal and the ITCN/IDL signals. It is desirable to integrate multi-input multi-output (MIMO) into ITCN/IDL transmission to increase the throughput and reduce the portion of spectrum occupation by ITCN/IDL. However, due to the backward compatibility constraint, conventional symmetrical MIMO techniques cannot be applied directly to the existing broadcast infrastructure without affecting legacy receivers. In this paper, the practical implementation of a non-symmetrical MIMO for ITCN/IDL while maintaining backward compatibility is considered. In addition to the existing broadcasting antenna, the non-symmetrical MIMO comprises an additional low-power RF feeder cable and one or more highly directional antennas to achieve low-cost MIMO implementation for high throughput data distribution and inter-tower communications. When the broadcast service signal and the MIMO signal are multiplexed in Time Division Multiplexing (TDM) format, the signal structure is ATSC 3.0 standard compliant. When the broadcast service signal and the MIMO signal are multiplexed in Layer Division Multiplexing (LDM) format, modification of the ATSC 3.0 standard, i.e., introducing a new pilot encoding scheme, is required. Moreover, MIMO self-interference cancellation (SIC) is investigated for IBFD operation.

Implementation of Wireless Backhaul and Inter-Tower Communications With MIMO in ATSC 3.0

Abstract: Wireless backhaul, i.e., in-band distribution links (IDL) and inter-tower communications networks (ITCN) were previously proposed as key enabling technologies for the next-generation digital broadcasting systems, e.g., the Advanced Television Systems Committee (ATSC) 3.0 system. Both ITCN/IDL can be operated in the spectrum-efficient in-band full-duplex (IBFD) mode and the same frequency band is shared between the broadcast service signal and the ITCN/IDL signals. It is desirable to integrate multi-input multi-output (MIMO) into ITCN/IDL transmission to increase the throughput and reduce the portion of spectrum occupation by ITCN/IDL. However, due to the backward compatibility constraint, conventional symmetrical MIMO techniques cannot be applied directly to the existing broadcast infrastructure without affecting legacy receivers. In this paper, the practical implementation of a non-symmetrical MIMO for ITCN/IDL while maintaining backward compatibility is considered. In addition to the existing broadcasting antenna, the non-symmetrical MIMO comprises an additional low-power RF feeder cable and one or more highly directional antennas to achieve low-cost MIMO implementation for high throughput data distribution and inter-tower communications. When the broadcast service signal and the MIMO signal are multiplexed in Time Division Multiplexing (TDM) format, the signal structure is ATSC 3.0 standard compliant. When the broadcast service signal and the MIMO signal are multiplexed in Layer Division Multiplexing (LDM) format, modification of the ATSC 3.0 standard, i.e., introducing a new pilot encoding scheme, is required. Moreover, MIMO self-interference cancellation (SIC) is investigated for IBFD operation.

Self-Supervised Representation Learning for Video Quality Assessment

Abstract: No-reference (NR) video quality assessment (VQA) is a challenging problem due to the difficulty in model training caused by insufficient annotation samples. Previous work commonly utilizes transfer learning to directly migrate pre-trained models on the image database, which suffers from domain inadaptation. Recently, self-supervised representation learning has become a hot spot for the independence of large-scale labeled data. However, existing self-supervised representation learning method only considers the distortion types and contents of the video, there needs to investigate the intrinsic properties of videos for the VQA task. To amend this, here we propose a novel multi-task self-supervised representation learning framework to pre-train a video quality assessment model. Specifically, we consider the effects of distortion degrees, distortion types, and frame rates on the perceived quality of videos, and utilize them as guidance to generate self-supervised samples and labels. Then, we optimize the ability of the VQA model in capturing spatio-temporal differences between the original video and the distorted version using three pretext tasks. The resulting framework not only eases the requirements for the quality of the original video but also benefits from the self-supervised labels as well as the Siamese network. In addition, we propose a Transformer-based VQA model, where short-term spatio-temporal dependencies of videos are modeled by 3D-CNN and 2D-CNN, and then the long-term spatio-temporal dependencies are modeled by Transformer because of its excellent long-term modeling capability. We evaluated the proposed method on four public video quality assessment databases and found that it is competitive with all compared VQA algorithms.

Performance Analysis and Power Allocation for Spatial Modulation-Aided MIMO-LDM With Finite Alphabet Inputs

Abstract: In this paper, we study a spatial modulation (SM) aided multiple-input multiple-output (MIMO) layered division multiplexing (LDM) system for broadcast/multicast service delivery in future broadcast/multicast systems. Comprehensive performance analysis and the injection level (IL) optimization are investigated for the SM-aided MIMO-LDM system over correlated Rayleigh fading channels. First, the symbol detection pairwise error probability (PEP) and the average symbol error rate (SER) union bound under joint maximum-likelihood (ML) detection are derived. Second, the spectral efficiency (SE) of the SM-aided MIMO-LDM system with finite alphabet inputs is analyzed. Since the theoretical SE lacks closed-form expression and involves prohibitive computational complexity, we then derive the closed-form lower bounds and tight approximations for the theoretical SE, in which the computational complexity is relieved by several orders of magnitude compared to direct calculation of the theoretical SE. Third, based on the SE analysis, a weighted sum (WS) SE maximization problem with quality of service (QoS) constraints is formulated to optimize IL for the SM-aided MIMO-LDM system. We first demonstrate that WS SE is a unimodal function of IL, and then develop an efficient golden section search (GSS) based algorithm. Simulation results are provided to validate the theoretical analysis. It is shown that our derived SER union bound well matches the simulated SER in the high SNR region. The tightness of the derived closed-form lower bounds and approximations for theoretical SE and the effectiveness of the developed IL optimization algorithm are also verified by simulation results.

In-Band Full-Duplex Communications in ATSC 3.0 Single Frequency Network

Abstract: Wireless in-band backhaul, a.k.a. in-band distribution links (IDL), is a spectrum-efficient and cost-effective enabling technology to the realization of Advanced Television Systems Committee (ATSC) 3.0 in the single frequency network (SFN) mode, where all the transmitter towers are synchronized to transmit the same broadcast signal in the same frequency band. Inter-tower communications network (ITCN) transforms the broadcast towers into a mesh network, thereby introducing datacasting capability to the traditional broadcast network. Both the ITCN and IDL can be operated in the most spectrum-efficient in-band full-duplex (IBFD) mode. In these situations, the ITCN/IDL receivers at the SFN towers receive the signal of interest (SOI) not only severely corrupted by the self-interference signal from its co-located transmitter, but also the co-channel interference signals from neighbouring transmitters. Moreover, the ITCN/IDL signals may be combined with the broadcast signal in the Layer Division Multiplexing (LDM) format to achieve better overall spectral efficiency. Therefore, the LDM inter-layer interference must also be mitigated. In this paper, the interferences for the ITCN/IDL signal in the SFN environment are analyzed, and a novel iterative successive signal cancellation scheme is proposed to effectively mitigate the interferences in the ITCN/IDL signal detection process in ATSC 3.0 SFNs.

In-Band Full-Duplex Communications in ATSC 3.0 Single Frequency Network

Abstract: Wireless in-band backhaul, a.k.a. in-band distribution links (IDL), is a spectrum-efficient and cost-effective enabling technology to the realization of Advanced Television Systems Committee (ATSC) 3.0 in the single frequency network (SFN) mode, where all the transmitter towers are synchronized to transmit the same broadcast signal in the same frequency band. Inter-tower communications network (ITCN) transforms the broadcast towers into a mesh network, thereby introducing datacasting capability to the traditional broadcast network. Both the ITCN and IDL can be operated in the most spectrum-efficient in-band full-duplex (IBFD) mode. In these situations, the ITCN/IDL receivers at the SFN towers receive the signal of interest (SOI) not only severely corrupted by the self-interference signal from its co-located transmitter, but also the co-channel interference signals from neighbouring transmitters. Moreover, the ITCN/IDL signals may be combined with the broadcast signal in the Layer Division Multiplexing (LDM) format to achieve better overall spectral efficiency. Therefore, the LDM inter-layer interference must also be mitigated. In this paper, the interferences for the ITCN/IDL signal in the SFN environment are analyzed, and a novel iterative successive signal cancellation scheme is proposed to effectively mitigate the interferences in the ITCN/IDL signal detection process in ATSC 3.0 SFNs.

An End-to-End Blind Image Quality Assessment Method Using a Recurrent Network and Self-Attention

Abstract: In this paper, we propose a blind image quality assessment (BIQA) method using self-attention and a recurrent neural network (RNN); this approach can effectively capture both local and global information from an input image. The implementation of our constructed deep no-reference (NR) assessment framework does not rely on any convolutional operations. First, the capture step for obtaining locally significant information is performed by a self-attention operation inside a divided window. Second, we design a serialized feature input memory subnetwork to fuse the global features of the image. Finally, all the integrated features are uniformly mapped to the target score. The experimental results obtained on publicly available benchmark IQA databases show that our approach outperforms other state-of-the-art algorithms.

Deep Neural Network for Blind Visual Quality Assessment of 4K Content

Abstract: The 4K content can deliver a more immersive visual experience to consumers due to the huge improvement in spatial resolution. However, the high spatial resolution brings a great challenge for video transmission and storage. Therefore, it is necessary to compress or downscale the 4K content before transmitting it to end-users. Existing blind image quality assessment (BIQA) methods are not suitable for 4K contents due to the high spatial resolution and specific distortions caused by upscaling methods. In this paper, we propose a deep learning-based BIQA model for 4K content, aiming to recognize true and pseudo 4K content and meanwhile evaluate their visual quality. Considering the characteristic that high spatial resolution can represent more abundant high-frequency information, we first propose a Grey-level Co-occurrence Matrix (GLCM) based texture complexity measure to select three representative image patches from a 4K image, which can reduce the computational complexity and is proven to be very effective for the overall quality prediction. Then, we extract various visual features from the intermediate layers of the convolutional neural network (CNN) and integrate them into the quality-aware feature representation. Finally, two multilayer perception (MLP) networks are utilized to map the quality-aware features into the class probability and the quality score of each patch respectively. The overall quality index is obtained through averaging the results of all patches. The proposed model is trained via the multi-task learning manner and the uncertainty principle is introduced to balance the losses of the classification and regression tasks. The experimental results show that the proposed model outperforms all compared BIQA metrics on four 4K content quality assessment databases.

Spatiotemporal Feature Hierarchy-Based Blind Prediction of Natural Video Quality via Transfer Learning

Abstract: In this paper, we propose a pyramidal spatiotemporal feature hierarchy (PSFH)-based no-reference (NR) video quality assessment (VQA) method using transfer learning. First, we generate simulated videos by a generative adversarial network (GAN)-based image restoration model. The residual maps between the distorted frames and simulated frames, which can capture rich information, are utilized as one input of the quality regression network. Second, we use 3D convolution operations to construct a PSFH network with five stages. The spatiotemporal features incorporating the shared features transferred from the pretrained image restoration model are fused stage by stage. Third, with the guidance of the transferred knowledge, each stage generates multiple feature mapping layers that encode different semantics and degradation information using 3D convolution layers and gated recurrent units (GRUs). Finally, five approximate perceptual quality scores and a precise prediction score are obtained by fully connected (FC) networks. The whole model is trained under a finely designed loss function that combines pseudo-Huber loss and Pearson linear correlation coefficient (PLCC) loss to improve the robustness and prediction accuracy. According to the extensive experiments, outstanding results can be obtained compared with other state-of-the-art methods. Both the source code and models are available online.1

Inter-Tower Communications Network Signal Structure, and Interference Analysis for Terrestrial Broadcasting and Datacasting

Abstract: This paper presents the Inter-Tower Communications Network (ITCN) signal structure using different combinations of multiplexing schemes, namely time-division multiplexing (TDM)/frequency-division multiplexing (FDM) and layer-division multiplexing (LDM). Data capacity is analyzed for mobile and fixed broadcasting services, as well as datacasting, inter-tower communication data, and SFN In-band Distribution Links (IDL). Multiple-input multiple-output (MIMO) is investigated for ITCN and IDL to increase the data throughput and to improve spectrum efficiency. The proposed technology can be Digital television (DTV) standard agnostic. It can be implemented on all existing DTV standards while keeping backward compatibility with legacy broadcast services and legacy TV receivers. A hybrid broadcast SFN overlay with ITCN/IDL data network using LDM might be the best solution considering backward compatibility, data capacity, and co-channel interference.

Hybrid-Mux Signal Structure and Resource Allocation for In-Band Distribution Link and ITND Transmission in SFN Environment

Abstract: This paper investigates the multiplexing and resource allocation to improve the spectrum efficiency and reduce demultiplexing complexity and latency in the wireless in-band distribution link (IDL) and inter-tower networks and datacasting (ITND) for terrestrial broadcasting systems. A novel Hybrid-Mux technology is proposed, which combines orthogonal multiplexing (TDM/FDM), non-orthogonal multiplexing (NOM, or LDM), and hierarchical modulation (HM) with the non-uniform constellation (NUC). Hybrid-Mux technology can achieve high spectrum efficiency, low complexity, and low latency to provide versatile services of mobile/fixed broadcast services, inter-tower communication networks and datacasting, as well as the in-band distribution link to support SFN operation. Two Hybrid-Mux signal structures with 2-layer and 3-layer power-based non-orthogonal multiplexing of different broadcast and network data services are introduced. The throughput optimization of each Hybrid-Mux structure is formulated and resolved under the constraint of ITND and IDL data capacity, SNR requirement, and optimized NUC HM. Simulation results show that, by properly implementing resource allocation and constellation design, the proposed 3-layer Hybrid-Mux structures can achieve higher capacity than the simple 2-layer Hybrid-Mux structure, while the demodulation and demultiplexing complexity is not much increased. A 2-tier ITND service is proposed. It consists of a robust control and datacasting (ITCD) tier and a high data rate intertower networking (ITCN) link. This design can increase the total aggregated system data rate substantively. MIMO structures supporting ITND and IDL transmission are also proposed to further improve the spectrum efficiency.

Evaluation of ATSC 3.0 and 3GPP Rel-17 5G Broadcasting Systems for Mobile Handheld Applications

Abstract: This paper provides performance evaluations of two state-of-the-art terrestrial broadcasting systems, ATSC 3.0 and 3GPP Rel-17 5G broadcast, in terms of physical layer capability, network deployment and operating costs. The physical layer performances are evaluated in the mobile broadcasting environment, considering practical implementations of the handheld terminals. Extensive simulation results demonstrate that ATSC 3.0 outperforms 5G broadcast, because of well-designed bit interleaved coded modulation (BICM) and time interleaver that can mitigate deep signal fades. The capital expenditures (CAPEX) and operating expenses (OPEX) of the two systems are analyzed based on physical layer performances. The evaluations could be used for the planning of terrestrial broadcast services to mobile handheld devices.

Hybrid-Mux Signal Structure and Resource Allocation for In-Band Distribution Link and ITND Transmission in SFN Environment

Abstract: This paper investigates the multiplexing and resource allocation to improve the spectrum efficiency and reduce demultiplexing complexity and latency in the wireless in-band distribution link (IDL) and inter-tower networks and datacasting (ITND) for terrestrial broadcasting systems. A novel Hybrid-Mux technology is proposed, which combines orthogonal multiplexing (TDM/FDM), non-orthogonal multiplexing (NOM, or LDM), and hierarchical modulation (HM) with the non-uniform constellation (NUC). Hybrid-Mux technology can achieve high spectrum efficiency, low complexity, and low latency to provide versatile services of mobile/fixed broadcast services, inter-tower communication networks and datacasting, as well as the in-band distribution link to support SFN operation. Two Hybrid-Mux signal structures with 2-layer and 3-layer power-based non-orthogonal multiplexing of different broadcast and network data services are introduced. The throughput optimization of each Hybrid-Mux structure is formulated and resolved under the constraint of ITND and IDL data capacity, SNR requirement, and optimized NUC HM. Simulation results show that, by properly implementing resource allocation and constellation design, the proposed 3-layer Hybrid-Mux structures can achieve higher capacity than the simple 2-layer Hybrid-Mux structure, while the demodulation and demultiplexing complexity is not much increased. A 2-tier ITND service is proposed. It consists of a robust control and datacasting (ITCD) tier and a high data rate intertower networking (ITCN) link. This design can increase the total aggregated system data rate substantively. MIMO structures supporting ITND and IDL transmission are also proposed to further improve the spectrum efficiency.

Hybrid Strategies for Efficient Intra Prediction in Spatial SHVC

Abstract: With multi-layer encoding and Inter-layer prediction, Spatial Scalable High Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to speed up its coding to promote widespread and cost-effective SSHVC applications. Specifically, we first reveal that the average RD cost of Inter-layer Reference (ILR) mode is different from that of Intra mode, but they both follow the Gaussian distribution. Based on this discovery, we apply the classic Gaussian Mixture Model and Expectation Maximization to determine whether ILR mode is the best mode thus skipping Intra mode. Second, when coding units (CUs) in enhancement layer use Intra mode, it indicates very simple texture is presented. We investigate their Directional Mode (DM) distribution, and divide all DMs into three classes, and then develop different methods with respect to classes to progressively predict the best DMs. Third, by jointly considering rate distortion costs, residual coefficients and neighboring CUs, we propose to employ the Conditional Random Fields model to early terminate depth selection. Experimental results demonstrate that the proposed algorithm can significantly improve coding speed with negligible coding efficiency losses.

Joint Visible Light Sensing and Communication Using <i>m</i>-CAP Modulation

Abstract: As 5G devices and networks continue to roll out, new broadcasting services and capabilities have been introduced to the entire ecosystem, opening up additional new applications and granular business opportunities, where indoor joint communication and sensing are critical. Under this trend, in this paper, we propose a new system using multi-band carrierless amplitude and phase $(m$ -CAP) modulation associated with received signal strength (RSS)-based trilateration to achieve visible light sensing and communication from the same signal. The architecture of this system is first detailed, with an emphasis on how the light source limitations in terms of dynamic range and modulation bandwidth may be taken into account. The proposed set-up is then shown through simulations to provide an illuminance between 300 and 500 lux over the whole room, positioning with an error lower than 7.17 cm in 90% of the cases, and a continuous data connectivity at 32 Mbps. The influence of several parameters, including that of the main $m$ -CAP settings, on this performance is studied in order to define some general rules for the design of such a system.

Feasibility of Backward Compatible MIMO Broadcasting: Issues in SISO-MIMO Coexistence

Abstract: This paper introduces the application of backward compatible (B-Comp) MIMO for simultaneously supporting inter-tower communications network (ITCN) and legacy digital television (DTV) service. In B-Comp MIMO systems, SISO and MIMO broadcasting are overlaid on the same frequency, where the SISO transmission is subject to compliance with legacy system protocol. In this work, the several configurations of B-Comp MIMO are proposed in terms of physical layer multiplexing (e.g., layered or time-division multiplexing) and antenna assignment in legacy transmission, and accordingly, system-specific issues on each configuration affecting the performance degradation or practical realization are discovered. Moreover, the spectral efficiency performance of each configuration is derived and analyzed under the system-specific issues in terms of the achievable throughput for ITCN while guaranteeing the legacy service’s requirements. Through such investigations with qualitative inspections, theoretic analysis, and numerical simulations, practical observations are modeled in a theoretic framework, and performance-objected tradeoffs are explicitly revealed to allow insightful decisions to answer which configuration is optimal for each use case. Lastly, the performance penalties from pilot-related limitations are delved into and hence a comprehensive inspection is allowed in a practical view.

Multilevel Feature Fusion for End-to-End Blind Image Quality Assessment

Abstract: In this paper, a framework based on two feature extraction networks and a multilevel feature fusion (MFF) network is proposed. Multilevel degradation features can be obtained through this method, and combined with the human visual perception system, the local and global feature information contained in these features can be captured, which is conducive to the prediction of distorted images. First, a restored image approximating a reference image is generated by a restorative generative adversarial network (GAN). Furthermore, the multilevel degradation features of distorted images and the restored image features are extracted by EfficientNet. Second, the features extracted by EfficientNet are input into the MFF network and are fully expressed by the top-down, bottom-up and third edge joining methods. Moreover, the features provide more low-level details and high-level semantic features for the prediction of image quality scores. In addition, after the MFF stage, the framework calculates the score of each branch feature and obtains the average quality score. Experimental results show that our method achieves greatly improved prediction accuracy and performance on five standard databases.

Hybrid Strategies for Efficient Intra Prediction in Spatial SHVC

Abstract: With multi-layer encoding and Inter-layer prediction, Spatial Scalable High Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to speed up its coding to promote widespread and cost-effective SSHVC applications. Specifically, we first reveal that the average RD cost of Inter-layer Reference (ILR) mode is different from that of Intra mode, but they both follow the Gaussian distribution. Based on this discovery, we apply the classic Gaussian Mixture Model and Expectation Maximization to determine whether ILR mode is the best mode thus skipping Intra mode. Second, when coding units (CUs) in enhancement layer use Intra mode, it indicates very simple texture is presented. We investigate their Directional Mode (DM) distribution, and divide all DMs into three classes, and then develop different methods with respect to classes to progressively predict the best DMs. Third, by jointly considering rate distortion costs, residual coefficients and neighboring CUs, we propose to employ the Conditional Random Fields model to early terminate depth selection. Experimental results demonstrate that the proposed algorithm can significantly improve coding speed with negligible coding efficiency losses.

The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems

Abstract: Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G) New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middle-range deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast beams to serve multicast users.

Inter-Tower Communications Network Signal Structure, and Interference Analysis for Terrestrial Broadcasting and Datacasting

Abstract: This paper presents the Inter-Tower Communications Network (ITCN) signal structure using different combinations of multiplexing schemes, namely time-division multiplexing (TDM)/frequency-division multiplexing (FDM) and layer-division multiplexing (LDM). Data capacity is analyzed for mobile and fixed broadcasting services, as well as datacasting, inter-tower communication data, and SFN In-band Distribution Links (IDL). Multiple-input multiple-output (MIMO) is investigated for ITCN and IDL to increase the data throughput and to improve spectrum efficiency. The proposed technology can be Digital television (DTV) standard agnostic. It can be implemented on all existing DTV standards while keeping backward compatibility with legacy broadcast services and legacy TV receivers. A hybrid broadcast SFN overlay with ITCN/IDL data network using LDM might be the best solution considering backward compatibility, data capacity, and co-channel interference.

Optimization of Partial Transmit Sequences for PAPR Reduction of OFDM Signals Without Side Information

Abstract: This paper develops a novel optimization of partial transmit sequences (PTS) with phase quantization to reduce the peak-to-average-power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM) signals and enable data detection without side information. Since the formulated optimization problem is non-convex and has exponential time complexity, we employ a convex relaxation method to convert the original optimization problem into a series of convex programming whose solutions converge to a sub-optimal point satisfying the Karush-Kuhn-Tucker (KKT) conditions in polynomial time. Moreover, the obtained phase factors are quantized to enable the maximum likelihood (ML) phase estimation at the receiver, hence removing the need of sending side information. Analytical and numerical results are provided to show that our proposed PTS design achieves better PAPR reduction over existing PTS methods, while no performance degradation is incurred in data detection when the number of phase factors is properly chosen.

Decentralized Optimization for Multicast Adaptive Video Streaming in Edge Cache-Assisted Networks

Abstract: Adaptive streaming based on DASH offers personalized video experience and smooth playback by allowing dynamical adjustments of the video bitrate to the variations of network conditions. This is especially important for current and future Internet video streaming applications, including emerging ones such as virtual reality-based, as adaptive streaming plays a key role in providing high quality viewing experience, especially in limited bandwidth delivery environments. To enable this promising avenue in a 5G context, efforts are made to consider it alongside multicast and edge caching, as part of the next generation communication technology. In this paper, we model the adaptive streaming transmission problem in a mobile scenario as a multi-source multicast multi-rate problem (MMMP) whose linear relaxation is concave. We decompose the problem in terms of clients and propose the distributed delivery algorithm (DDA). The computation complexity, convergence and time-varying adaptation of DDA are theoretically analyzed. Additionally, to further reduce the computation complexity of the solution, a heuristic approximation method (H-DDA) based on the physical meaning of the problem is proposed and it is also shown how H-DDA converges to the optimal value by numerical means. Finally, we conduct a series of simulation tests to demonstrate the superiority of the proposed HDDA in comparison with other state-of-art solutions.

Out-of-Band Digital Predistortion for Power Amplifiers With Strong Nonlinearity

Abstract: Conventional full-band digital predistortion (DPD) suppresses the inband (IB) distortion to improve the error vector magnitude (EVM) performance and simultaneously suppresses the out-of-band (OOB) distortion to reduce the adjacent-channel interference. However, its performance is very limited when the nonlinearity is strong, especially in terms of suppressing the OOB distortion. This paper proposes an OOB DPD scheme for strong nonlinearity with a special focus on suppressing the OOB distortion to reduce the adjacent-channel interference. The proposed OOB DPD is featured by specifying an arbitrary OOB-distortion band for linearization and controlling the improvement level of EVM performance. Experiments demonstrate that the proposed OOB DPD can achieve superior performance in suppressing the OOB distortion when the nonlinearity is strong. We also present analyses to show that in strong nonlinearity cases with high compression, focusing on suppressing the OOB distortion at a specific band is an effective way to improve the suppression level of the target distortion. The proposed OOB DPD can further improve the transmission power under the assurance of low adjacent-channel interference. It can be applied in communications with urgent needs in high transmission power, high efficiency, and low adjacent-channel interference, such as military communications that usually adopt low-order modulations with low EVM requirements.