Rate Control Scheme Research Articles

Module‐Based Supercapacitors: Potential Energy Storage Solutions for Large‐Scale Photovoltaic Systems

Intermittency is an inherent characteristic of photovoltaic (PV) power generation and results in high ramp rates of the generated power. This article explores the feasibility of integrating supercapacitors at the PV module level, aiming to reduce the power fluctuations of PV systems and control the power ramp rate into the power grid. First, an equivalent circuit model of a single‐phase grid‐connected PV system based on module‐based supercapacitors is proposed, and a power ramp rate control scheme is established. Then, experimental setups for a single‐phase grid‐connected PV system based on module‐based supercapacitors are implemented, and the computational model is verified through experiments. Finally, using the verified computational model and the proposed control scheme, the module‐based supercapacitor sizes for different PV system sizes (PV module, rooftop, small system, large system) that meet specific ramp rate requirements under different ramp rate limits (5, 10, 15% min−1) are compared. Case studies show that large‐scale PV systems with geographical smoothing effects help to reduce the size of module‐based supercapacitors per normalized power of installed PV, providing the possibility for the application of modular supercapacitors as potential energy storage solutions to improve power ramp rate performance in large‐scale PV systems.

Region-of-Interest Based Coding Scheme for Live Videos

In this paper, we introduce a novel rate control scheme specifically tailored for live broadcasting scenarios. Notably, in high-definition live transmissions of sports events and video game competitions that typically exceed 1080 p resolution and run at frame rates of 60 fps or higher, the transcoding speed of encoders often becomes a limiting factor, leading to streams with substantial bitrates but unsatisfactory quality metrics. To enhance the overall Quality of Service (QoS) without increasing the bitrate, it is essential to improve the quality of Regions of Interest (ROI).Our proposed solution presents an ROI-based rate reservoir model that ingeniously leverages Convolutional Neural Networks (CNNs) to predict rate control parameters. This approach aims to optimize the bitrate allocation within high bitrate live broadcasts, thus enhancing the image quality within ROIs. Experimental outcomes demonstrate that this algorithm manages to increase the bitrate by no more than 5%, effectively redistributing the reduced bitrate across the entire Group of Pictures (GOP). As a result, it ensures a gradual decrease in the quality of Regions of Uninterest (ROU), thereby maintaining a balanced quality experience throughout the broadcasted content.

A Rate Control Scheme for VVC Intercoding Using a Linear Model

Versatile video coding (VVC) aims to achieve high compression but also issues like varying content/network conditions. Existing rate control (RC) methods struggle to achieve optimal quality under these complex scenarios. This paper proposes a novel RC scheme for VVC based on a linear model. The Lagrange minimization multiplier is introduced under bit budget constraints, allowing optimized bit allocation. RC optimization is formulated as a convex solution, and is derived into the optimal quantization parameter (QP) for RC. Experimental analysis demonstrates the proposed linear model-based RC algorithm performances are better compared to other state-of-the-art methods due to their use of a linear model and optimal QP determination.

Learning From Coding Features: High Efficiency Rate Control for AOMedia Video 1

Rate control, which typically includes bit allocation and quantization parameter (QP) determination, plays an important role in real-world video coding applications. In this paper, we propose a novel rate control scheme for AOMedia Video 1 (AV1) which enjoys adaptive bit allocation and effective QP determination. In particular, two supporting vector regression (SVR) models are learned for the hierarchical bit allocation and frame-level parameter estimation. To train the models, the multi-pass coding strategy is utilized for training data acquisition. Compared to the default scheme in AV1 and the state-of-the-art method, the proposed rate control scheme achieves superior performance in terms of bitrate accuracy and coding efficiency.

A CTU-Level Screen Content Rate Control for Low-Delay Versatile Video Coding

In this paper, a rate control scheme for screen content video coding is proposed for the Versatile Video Coding (VVC) standard. In view of the critical challenges arising from the spatial and temporal unnaturalness of screen content sequences, the proposed method relies on the specifically designed pre-analysis such that the content information regarding the scene complexity can be obtained. As such, the estimated residual complexity is then incorporated into the proposed complexity-aware rate models and distortion models, leading to the optimal bit allocations for each frame and coding tree unit (CTU). In particular, the optimization problem can be analytically solved with the proposed models, and the coding parameters such as Lagrangian multiplier λ and quantization parameter of each frame and CTU could be delicately derived according to the allocated bits through the proposed analytical models. Extensive experiments have been conducted to evaluate the effectiveness of the proposed method. Compared to the default hierarchical λ-domain rate control and other screen content rate control algorithms, the proposed method could achieve obvious RD performance gain, and the bit-rate accuracy could be improved.

Experimental assessment and modeling of radiant cooling technology using constant and variable water flow rate

The goal of this study is to perform an analysis of the performance of radiant cooling panels using experimental testing and transient system simulation software. The effect of design parameters such as water supply temperature and water flow rate on thermal comfort and energy efficiency is studied. Two control schemes are used in the study in which either the water flow rate or the water temperature difference between the inlet and outlet of radiant panels is held constant. This paper demonstrates the modeling and validation of a radiant cooling system in TRNSYS and uses this model to study the performance of the system during the cooling season in Egypt. It was found that the lower water temperature of 16 °C was found to be suitable for achieving thermal comfort. The constant water flow rate control scheme showed a 5% less unmet hours percentage than the constant water temperature difference, but the latter used 38% less electrical energy. A constant water temperature difference control scheme can achieve better thermal comfort with an unmet hours percentage down to 12.5% by coupling the system with a dehumidification device.

Rate Control in Versatile Video Coding with Cosh Rate–Distortion Model

In this paper, we propose a rate control (RC) scheme for versatile video coding (VVC) to enhance coding performance. First, we propose a rate–distortion (R–D) model at the coding tree unit (CTU) level to describe the R–D relationship. Second, we adjust the quantization parameter (QP) and Lagrange multiplier at the coding unit (CU) level according to visual features to improve the rationality of local coding results. Finally, we propose a model parameter updating strategy to guarantee bitrate accuracy. The experimental results demonstrate that our RC method has better R–D performance with similar bitrate accuracy and better visual quality compared to the default RC strategy in VVC reference software VTM 10.2.

SUR-Driven Video Coding Rate Control for Jointly Optimizing Perceptual Quality and Buffer Control.

Rate control plays an important role in video coding and has attracted lots of attention from researchers. However, the problems of human visual experience and buffer stability still remain. For scenes with drastic motions, parts of distortions can be masked due to the limitation of the Human Visual System (HVS), while buffers tend to suffer more overflow and underflow cases from the fluctuating bits. In this paper, we propose a novel joint rate control scheme, which is composed of the proposed SUR-based perception modeling and the proposed SUR-based Perception-Buffer Rate Control (PBRC), for HEVC to maximize human visual perception quality while preventing the underflow and overflow of buffers. First of all, to effectively model human visual quality, we introduce the perception-related Satisfied-User-Ratio (SUR) metric into the rate control process. Secondly, a time-efficient video quality prediction method called Fast Visual Multimethod Assessment Fusion (VMAF) Quality Prediction (FVQP) is designed for the generation of SUR curves within an affordable computational complexity. Thirdly, a dual-objective optimization framework is established. By jointly conducting perception modeling and PBRC, we can flexibly adjust the optimization priority between human visual quality and buffer stability, and thus the quality of achieved reconstructed videos can be effectively improved because of the decrease in frame skipping. Experimental results demonstrate that the proposed joint rate control scheme improves the human visual experience when considering frame skipping and more effectively stabilizes buffer stability than existing methods.

Joint Decision Tree and Visual Feature Rate Control Optimization for VVC UHD Coding.

In this paper, a joint decision tree and visual feature optimization rate control scheme for ultrahigh-definition (UHD) versatile video coding (VVC) is proposed. First, we design a new rate-distortion (R-D) model for UHD videos, and we establish a decision-tree-based multiclass classification scheme to improve the prediction accuracy of the R-D model by fully considering visual features. Second, based on the proposed R-D model, the globally optimal solution is obtained through convex optimization. Finally, we embed our algorithm into the latest VVC reference software, VTM 10.2. According to our experimental results, compared with the latest algorithm in VTM 10.2 and other state-of-the-art algorithms, our method can achieve significant bit rate reductions while maintaining a given peak signal-to-noise ratio (PSNR) or structural similarity index measure (SSIM).

Information freshness optimization of multiple status update streams in Internet of things: Generation rate control and service rate reservation

The Internet of things (IoT) has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications. However, there are limited sensing and communication resources in IoT devices, which significantly affects the timeliness and freshness of the updated status. This work proposes two schemes, namely, the generation rate control and service rate reservation schemes, to improve the overall information freshness of multiple status update streams at the receiver. Specifically, using the recently proposed Age of Information (AoI) as the metric for evaluating information freshness, we characterized the overall information freshness, i.e., the overall average AoI at the receiver for both schemes, by considering the urgency difference of status update and streams. Both schemes for status updates and streams, respectively, were formulated as two optimization problems. We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm. Moreover, we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases. Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions, guiding the design and deployment of IoT.

A Flow-Limited Rate Control Scheme for the Master–Slave Hydraulic Manipulator

Rate control is the most used operation method for master–slave hydraulic manipulators. However, the trajectory from the master controlled by the operator cannot completely meet the dynamic limitations and flow restriction of the hydraulic manipulator. Continuous large errors and oscillation will occur due to flow restrictions and high-order dynamics beyond the dynamic boundaries that significantly limits the velocity tracking performance of a hydraulic manipulator. In this article, we propose a flow-limited rate control scheme for the master–slave hydraulic manipulators. The variable velocity mapping is developed, adjusting the velocities to meet the pump and valves’ flow limits. An adaptive robust rate controller of hydraulic manipulators is designed. An adaptive boundary estimator is introduced to estimate the hydraulic manipulator acceleration boundaries, and a variable structure controller is used to plan the velocities to meet dynamic limits. The proposed control scheme is tested on a hydraulic manipulator. The results show that the control scheme reduced velocity error and avoid the manipulator vibration, so the performance is improved for the hydraulic manipulator.

AI-Based Saliency-Aware Video Coding

The demand for video through over-the-top (OTT) has been constantly increasing in recent years. During the COVID-19 pandemic, demand skyrocketed, hence leading to the need for better video compression. The human visual system (HVS) can quickly select visually important regions in its visual field. These regions are captured at high resolution, while other peripheral regions receive little attention. Saliency maps are a way to imitate the HVS attention mechanism. Recently, deep learning-based saliency models have achieved tremendous improvements. This article leverages state-of-the-art deep learning-based saliency models to improve video coding efficiency. First, a saliency-based rate control scheme is integrated in a high-efficiency video encoder (HEVC). Then, a saliency-guided preprocessing filtering step is introduced. Finally, the two approaches are combined. Objective and subjective evaluations show that it can lower the bitrate from 6% to almost 30% while maintaining the same visual quality.

Digital and Intelligent Image Processing by Artificial Intelligence and Internet of Things Technology in Sports Fitness Detection

With the development and application of the Internet of Things, cloud computing, Artificial Intelligence, big data, and other information technologies, sports fitness has ushered in new development opportunities. Intelligent sports detection has emerged as the times require. This paper takes intelligent physical fitness as the research object, explores the application of digital and intelligent image processing in physical fitness, and provides a theoretical reference for popularizing and implementing intelligent physical fitness in life. Firstly, the treadmill is an example to study the current situation of fitness equipment, especially the intelligent digital treadmill, and the future development trend of fitness equipment. Secondly, the upper and lower computer control scheme is proposed. The upper computer control scheme is selected, and the industrial computer is analyzed. The motion model of the main control motor and the calculation model of the heart rate control scheme and calorie consumption are established. Finally, the software and algorithm design of the control system are summarized, and necessary debugging, experiment, and analysis are carried out. The results show that: (1) the treadmill industrial control system can correctly receive the data sent by the Automatic Voltage Regulator (AVR) and express it in the form of images, which completely matches the asynchronous communication data format. (2) The ultrasonic distance detection experiment proves that the distance precision between the operating platform and the human body is extremely high, and its digital intelligent treadmill can meet human perception and corresponding control requirements well. (3) The heart rate pulse of the human body can be collected based on digital technology. The waveform displayed in the image is relatively stable and complete.

Review of PV power ramp rate control methods and their requirements

Grid codes play a significant role in setting power system characteristics, such as ramp rate of different kinds of power plants. Using novel energy sources, like solar energy, for supplying electrical power demands, develops the requisite for controlling the characteristics of these sources such as their power ramp rates. While solar power is going to increase or decrease, ramp rate control is crucial. There have been suggested different strategies for power ramp rate control. Some of these strategies need batteries and some do not. Due to solar radiation uncertainties, it is necessary to use energy storage systems to control power ramp rate. On the other hand, batteries add huge costs and unreliability to the power systems. As a result, there is a trading-off among initial cost, reliability, security, and operation costs for selecting an optimal strategy. Also significance of the grid is a decisive factor. In this paper, some power ramp rate control schemes are discussed, and the advantages and disadvantages of different types of schemes have been analyzed. Also, in this literature different kinds of power ramp rate control strategies for photovoltaics have been clustered into some major sets. Besides, power ramp rate control is essential for novel schemes such as virtual synchronous machines, so emerging virtual inertia in novel power grids is another motivation to establish different methods for controlling the power ramp rate of renewable energy sources. Thus, in this paper, there is a section discussing the applications of power ramp rate control schemes.

Region-level bit allocation for rate control of 360-degree videos using cubemap projection

Featuring with more uniform sampling density in the sphere domain and less non-uniform geometric deformations in the planar domain, variants of cubemap projection (CMP) format enable the higher compression ratio in on-going 360-degree video coding standardization. Different from single-view videos, 360-degree CMP videos feature with content discontinuity combined with the abrupt change of motion vectors between some adjacent faces. However, there is few bit allocation scheme designed for rate control of video coding of CMP format. Thus, this paper proposes a region-level bit allocation scheme for rate control of interframe coding of CMP format. The proposed scheme consists of two parts. The first part is machine learning based high HEVC coding cost region detection for individual faces, where the feature descriptor of a CTU consists of the face based texture complexity, motion magnitude, motion density, and temporal coherence of motion vector. The second part is fitting function based region-level bit allocation. Different from previous work, bits are assigned to the high coding cost region and non-high coding cost region in individual faces of CMP format. Experimental results indicate that the proposed scheme achieves higher bitrate accuracy and larger BD-WS-PSNR compared with the original rate control scheme of the reference software of HEVC, HM16.16 with the 360Lib.

Coastal transportation system green policy design model based on shipping network design

ABSTRACT As governments of various countries attach importance to green transportation, governments have focused their attention to understanding how they can shift the cargo shipping in coastal areas from land to water. Motivated by this, this paper establishes a green policy design model for coastal transportation systems. The model aims to minimise the comprehensive cost (i.e. the sum of the transportation cost and carbon emission tax) in the research area and optimise the government's shipping network design scheme and waterway freight rate control scheme under the established subsidy level. An active set algorithm is designed that can explore the local optimal solution of the model. Taking the Bohai Rim region of China as the research area, sensitivity analyses of different subsidy scales and origin-destination demand scales are carried out. The results show that the government's policy schemes will lead to a significant shift in the mode of transportation used for cargo.

Long-term rate control for concurrent multipath real-time video transmission in heterogeneous wireless networks

Concurrent multipath transmission provides an effective solution for streaming high-quality mobile videos in heterogeneous wireless networks. Rate control is commonly adopted in multimedia communication systems to fully utilize the available network bandwidth. This paper proposes a novel rate control for concurrent multipath video transmission. The existing rate control algorithms mainly adapt bit rate in the short-term pattern, i.e., without considering the long-term video transmission quality. We propose a long-term rate control scheme that takes into account the status of both the transmission buffer and video frames. First, a mathematical model is developed to formulate the non-convex problem of long-term quality maximization. Second, we develop a dynamic programming solution for online encoding bit rate control based on buffer status. The performance evaluation is conducted in a real test bed over LTE and Wi-Fi networks. Experimental results demonstrate that the proposed long-term rate control scheme achieves appreciable improvements over the short-term rate control schemes in terms of video quality and delay performance.

Adaptive Queue Management and Traffic Class Priority Based Fairness Rate Control in Wireless Sensor Networks

Congestion control in Wireless Sensor Networks (WSNs) is one of the key areas of research and different algorithms have been proposed using either of the notions of fair rate allocation, traffic class priority, and queue management. Use of the any one of the above is not adequate to address the challenges. Hence, in this paper, we have proposed a novel congestion control algorithm using the combined notions of fair allocation of bandwidth, prioritizing traffic classes, and Adaptive Queue Management (ADQM). The proposed Weighted Priority based Fair Queue Gradient Rate Control (WPFQGRC) scheme achieves the fair distribution of spare bandwidth by considering the traffic class priority, average queue size, and the connected loads of a node. Average queue size at every node is adapted based on the proposed notion of the gradient of the differential of Global Priority (GP) with respect to the differential of queue size. The output rate of a given node is computed based on the GP of the node and the average queue size. The spare bandwidth of a node is fairly distributed by taking into account the connected load of the given node. The proposed algorithm is developed to suit to a general topology of WSN, however for the sake of illustration, we have considered a tree topology network that deals with both Real-Time (RT) and Non-Real Time (NRT) traffic classes. The proposed algorithm is implemented in NS3 platform in Linux environment and the performance of the algorithm is evaluated in terms of throughput, packet loss, packet delay, traffic class patterns, node mobility, and the average queue size. The performance of the proposed algorithm is found to be superior to that of Yaghmaee et al. ’s, Brahma et al. ’s, Monowar et al. ’s, Sarode et al. ’s, Difference of Differentials Rate Control (DDRC), Weighted Priority based DDRC (WPDDRC), and Priority based Fairness Rate Control (PFRC) algorithms respectively.

RCOAP: A Rate Control Scheme for Reliable Bursty Data Transfer in IoT Networks

RCOAP: A Rate Control Scheme for Reliable Bursty Data Transfer in IoT Networks

Content-aware rate control scheme for HEVC based on static and dynamic saliency detection

High efficiency video coding (HEVC) greatly outperforms previous standards H.264/AVC in terms of coding bit rate and video quality. However, it does not take into account the human visual system (HVS), that people pay more attention to specific areas and moving objects. In this paper, we present a content-aware rate control scheme for HEVC based on static and dynamic saliency detection. The proposed strategy mainly consists of three techniques, static saliency detection, dynamic saliency detection, and adaptive bit rate allocation. Firstly, we train a deep convolution network (DCN) model to extract the static saliency map by highlighting semantically salient regions. Compared to traditional texture-based or color-based region of interest (ROI) extraction techniques, our models are more in line with the HVS. Secondly, we develop a moving object segmentation technique to automatically extract the dynamic salient regions for each frame. Furthermore, according to the fusion saliency map, a coding tree unit (CTU) level bit control technique is exploited to realize flexible and adaptive bit rate allocation. As a result, the quality of salient regions is improved by allocating more bits, while allocating fewer bits to the non-salient regions. We verified the proposed method on both the JCT-VC recommended data set and eye-tracking data set. Experiment results show that the PSNR of salient regions can improve by an average of 1.85 dB without adding bit rate burden, which significantly improves the visual experience.