Optimal Bit Research Articles

Content-Adaptive Bitrate Ladder Estimation in High-Efficiency Video Coding Utilizing Spatiotemporal Resolutions

The constant increase in multimedia Internet traffic in the form of video streaming requires new solutions for efficient video coding to save bandwidth and network resources. HTTP adaptive streaming (HAS), the most widely used solution for video streaming, allows the client to adaptively select the bitrate according to the transmission conditions. For this purpose, multiple presentations of the same video content are generated on the video server, which contains video sequences encoded at different bitrates with resolution adjustment to achieve the best Quality of Experience (QoE). This set of bitrate–resolution pairs is called a bitrate ladder. In addition to the traditional one-size-fits-all scheme for the bitrate ladder, context-aware solutions have recently been proposed that enable optimum bitrate–resolution pairs for video sequences of different complexity. However, these solutions use only spatial resolution for optimization, while the selection of the optimal combination of spatial and temporal resolution for a given bitrate has not been sufficiently investigated. This paper proposes bit-ladder optimization considering spatiotemporal features of video sequences and usage of optimal spatial and temporal resolution related to video content complexity. Optimization along two dimensions of resolution significantly increases the complexity of the problem and the approach of intensive encoding for all spatial and temporal resolutions in a wide range of bitrates, for each video sequence, is not feasible in real time. In order to reduce the level of complexity, we propose a data augmentation using a neural network (NN)-based model. To train the NN model, we used seven video sequences of different content complexity, encoded with the HEVC encoder at five different spatial resolutions (SR) up to 4K. Also, all video sequences were encoded using four frame rates up to 120 fps, presenting different temporal resolutions (TR). The Structural Similarity Index Measure (SSIM) is used as an objective video quality metric. After data augmentation, we propose NN models that estimate optimal TR and bitrate values as switching points to a higher SR. These results can be further used as input parameters for the bitrate ladder construction for video sequences of a certain complexity.

BER and outage throughput analysis of DPIM/DHPIM coded QPSK-OFDM based outdoor optical wireless communications

With the growing demand for fast and reliable communication systems, particularly in outdoor environments, it is essential to investigate advanced encoding techniques. Digital Pulse Interval Modulation (DPIM) and Dual Header Pulse Interval Modulation (DHPIM) emerge as promising alternatives to traditional line coding methods, providing enhanced spectral efficiency and resistance to signal disruptions. This paper presents the performance of a Quadrature Phase-Shift Keying (QPSK) Orthogonal Frequency Division Multiplexing (OFDM)-based Optical Wireless Communication (OWC) system using these advanced encoding schemes. The analysis includes simulation results on QPSK-OFDM-based transmitter design, free space optical channel modeling based on Gaussian and log-normal distribution atmospheric turbulence, and recovery of input digital stream using the mentioned line coding techniques. The results demonstrate optimal bit error rate (BER) values for the QPSK-OFDM-based OWC system. The primary innovation of this research lies in the encoding schemes and their performance in outdoor optical wireless communication systems under turbulent conditions. Through extensive simulations and analysis, detailed insights into the bit error rate and outage throughput characteristics of DPIM/DHPIM coded QPSK-OFDM are provided, offering a unique perspective on the performance of these schemes in real-world scenarios. The findings not only highlight the optimal BER values achievable with the QPSK-OFDM-based OWC system but also offer insights into the system's ability to overcome transmission challenges under various atmospheric conditions.

Energy and QoE Optimization for Mobile Video Streaming with Adaptive Brightness Scaling

Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context. Their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO , a novel user- P erception-based video E xperience O ptimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between a device’s state of motion, video quality and the resulting user-perceived quality. Specifically, by capturing the motion of the on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function and adapt to user preference, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.

Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression

In recent years, video data have increased in size, which results in enormous transmission pressure. Rate control plays an important role in stabilizing video stream transmissions by balancing the rate and distortion of video compression. To achieve high-quality videos through low-bandwidth transmission, video multi-scale-based end-to-end rate control is proposed. First, to reduce video data, the original video is processed using multi-scale bicubic downsampling as the input. Then, the end-to-end rate control model is implemented. By fully using the temporal coding correlation, a two-branch residual-based network and a two-branch regression-based network are designed to obtain the optimal bit rate ratio and Lagrange multiplier λ for rate control. For restoring high-resolution videos, a hybrid efficient distillation SISR network (HEDS-Net) is designed to build low-resolution and high-resolution feature dependencies, in which a multi-branch distillation network, a lightweight attention LCA block, and an upsampling network are used to transmit deep extracted frame features, enhance feature expression, and improve image detail restoration abilities, respectively. The experimental results show that the PSNR and SSIM BD rates of the proposed multi-scale-based end-to-end rate control are −1.24% and −0.50%, respectively, with 1.82% rate control accuracy.

Improving the latency for 5G/B5G based smart healthcare connectivity in rural area

Smart hospitals are poised to greatly enhance life quality by offering persistent health monitoring capabilities. Remote healthcare and surgery, which are highly dependent on low latency, have seen a transformative improvement with the advent of 5G technology. This has facilitated a new breed of healthcare services, including monitoring and remote surgical procedures. The enhanced features of 5G, such as Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC), have enabled the development of advanced healthcare systems. These systems reduce the need for direct patient contact in hospitals, which is especially pertinent as 5G becomes more widespread. This research presents novel hybrid detection algorithms, specifically QR decomposition with M-algorithm maximum likelihood-minimum mean square error (QRM-MLD-MMSE) and QRM-MLD-ZF (zero forcing), for use in Massive MIMO (M-MIMO) technology. These methods aim to decrease the latency in MIMO-based Non-Orthogonal Multiple Access (NOMA) waveforms while ensuring optimal bit error rate (BER) performance. We conducted simulations to evaluate parameters like BER and power spectral density (PSD) over Rician and Rayleigh channels using both the proposed hybrid and standard algorithms. The study concludes that our hybrid algorithms significantly enhance BER and PSD with lower complexity, marking a substantial improvement in 5G communication for smart healthcare applications.

GreenABR+: Generalized Energy-Aware Adaptive Bitrate Streaming

Adaptive bitrate (ABR) algorithms play a critical role in video streaming by making optimal bitrate decisions in dynamically changing network conditions to provide a high quality of experience (QoE) for users. However, most existing ABRs suffer from limitations such as predefined rules and incorrect assumptions about streaming parameters. They often prioritize higher bitrates and ignore the corresponding energy footprint, resulting in increased energy consumption, especially for mobile device users. Additionally, most ABR algorithms do not consider perceived quality, leading to suboptimal user experience. This paper proposes a novel ABR scheme called GreenABR+, which utilizes deep reinforcement learning to optimize energy consumption during video streaming while maintaining high user QoE. Unlike existing rule-based ABR algorithms, GreenABR+ makes no assumptions about video settings or the streaming environment. GreenABR+ model works on different video representation sets and can adapt to dynamically changing conditions in a wide range of network scenarios. Our experiments demonstrate that GreenABR+ outperforms state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 57% in data consumption while providing up to 25% more perceptual QoE due to up to 87% less rebuffering time and near-zero capacity violations. The generalization and dynamic adaptability make GreenABR+ a flexible solution for energy-efficient ABR optimization.

Video quality adaptation using CNN and RNN models for cost-effective and scalable video streaming Services

Video streaming services require adaptive bit rate strategies that optimize video quality based on network conditions and user preferences to provide a cost-effective and scalable solution. In this manuscript, we present a novel architecture that utilizes a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract features from the video stream and predict optimal bit rates for each frame. The CNN feature extractor extracts relevant features from the input video stream, which are then passed on to the RNN predictor, where the optimal bit rate is predicted to ensure a personalized viewing experience tailored to the current network conditions and specific user preferences. Our experimental results demonstrate that the proposed architecture achieves significant improvements in video quality while minimizing bandwidth usage and providing a better user experience. Specifically, our results show a 37.1% improvement in average bit rate, indicating that the proposed architecture optimizes the video quality and reduces bandwidth usage by 37.1% on average. We also observed a 16.6% improvement in Quality of Experience (QoE), meaning that users will perceive the video quality as better. Additionally, rebuffering was reduced by 87.5%, indicating that users enjoy smoother video playback without interruptions. Importantly, our architecture is optimized for Dynamic Adaptive Streaming over HTTP (DASH), addressing the need for efficient streaming over the most widely used protocol in the industry.

High capacity reversible data hiding in radiographic images with optimal bit allocation

This paper extends and improves the performance of a digital reversible watermarking algorithm based on histogram shifting presented in previous works. The considered algorithm exploits the property of image histograms of some kinds of medical images which present many contiguous 0-runs, i.e., a comb structure in the gray level frequencies. In particular, radiographic images exhibit this structure after contrast enhancement during the acquisition process. The previous work suggested performing gray-level histogram shifting according to a local optimization technique. In this paper, we apply combinatorial optimization techniques to entire blocks of contiguous 0-runs using a non-linear objective function transformed to fit a linear optimization algorithm. The obtained results show a meaningful improvement in the payload capacity of the original data-hiding method. A mild Peak Signal-to-Noise Ratio (PSNR) reduction is still acceptable for a qualitative preview of the images, which can be completely restored to their original cover form thanks to the reversibility of the method.

COCKTAIL: Video streaming QoE optimization with chunk replacement and guided learning

Adaptive bitrate (ABR) algorithms aim to provide the best user quality of experience (QoE) under fluctuating operating environments. Prior ABR protocols address the QoE maximization problem with a plethora of approximate optimization techniques including model predictive control (MPC), Lyapunov optimization, and deep reinforcement learning (DRL). Even though these algorithms provide adequate performances, they focus only on bitrate selections, precluding the chunk replacement option. We proclaim that chunk replacement can enhance the QoE if duplicated downloading is carefully administered. Moreover, we point out that bitrate selection and chunk replacement should be closely coupled into the optimization problem to fully realize the potential of chunk replacement. We first formulate a novel optimization problem with an expanded decision space that encompasses chunk replacement as well as bitrate selection. We then propose COCKTAIL, a DRL-based ABR algorithm that discovers efficient solutions to the new optimization problem by using several learning techniques. Experiments on real-world network traces show that COCKTAIL outperforms state-of-the-art baselines with improvements up to 16.1% in average QoE.

Neural Network Based Rate Control for Versatile Video Coding

In this work, we propose a neural network based rate control algorithm for Versatile Video Coding (VVC). The proposed method relies on the modeling of the Rate-Quantization (R-Q) and Distortion-Quantization (D-Q) relationships in a data driven manner based upon the characteristics of prediction residuals. In particular, a pre-analysis framework is adopted, in an effort to obtain the prediction residuals which govern the Rate-Distortion (R-D) behaviors. By inferring from the prediction residuals with deep neural networks, the Coding Tree Unit (CTU) level R-Q and D-Q model parameters are derived, which could efficiently guide the optimal bit allocation. Subsequently, the coding parameters, including Quantization Parameter (QP) and λ, at both frame and CTU levels, are obtained according to allocated bit-rates. We implement the proposed rate control algorithm on VVC Test Model (VTM-13.0). Experimental results exhibit that the proposed rate control algorithm achieves 0.77% BD-Rate savings under Low Delay B (LDB) configurations when compared to the default rate control algorithm used in VTM-13.0. For Random Access (RA) configurations, 1.77% BD-Rate savings can be observed. Furthermore, with better bit-rate estimation, more stable buffer status can be observed, further demonstrating the advantages of the proposed rate control method.

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.

Deep Hybrid Compression Network for Lidar Point Cloud Classification and Segmentation

Extensive research on deep neural networks for LiDAR point clouds has contributed inexhaustible momentum to the development of computer 3D vision applications. However, storage and energy consumption have always been a challenge for deploying these deep models on mobile devices. Quantization provides a feasible route, of which current primary research is focused on uniform bit-width quantization without considering different layers or filters’ sensitivity to different bit-widths. This article proposes a novel hybrid compression method based on relaxed mixed-precision quantization, relaxed weights pruning, and knowledge distillation to overcome the limitations of uniform quantization illustrated above, while further improving model accuracy and reducing model memory consumption. It employs a differentiable searching method to search for the optimal bit allocation and weight sparsity, while conducting feature distillation, accordingly considering the feature degradation by pooling operation in point cloud deep models. The proposed method combines three compression techniques to balance the trade-off between compression accuracy and model size. Pruning alleviates the increasing memory consumption problem caused by mixed-precision quantization, while distillation improves compression accuracy without increasing model size. The experiments validate that the proposed method outperforms state-of-the-art typical uniform quantization methods in terms of accuracy with an acceptable and relatively competitive compression performance.

OMPQ: Orthogonal Mixed Precision Quantization

To bridge the ever-increasing gap between deep neural networks' complexity and hardware capability, network quantization has attracted more and more research attention. The latest trend of mixed precision quantization takes advantage of hardware's multiple bit-width arithmetic operations to unleash the full potential of network quantization. However, existing approaches rely heavily on an extremely time-consuming search process and various relaxations when seeking the optimal bit configuration. To address this issue, we propose to optimize a proxy metric of network orthogonality that can be efficiently solved with linear programming, which proves to be highly correlated with quantized model accuracy and bit-width. Our approach significantly reduces the search time and the required data amount by orders of magnitude, but without a compromise on quantization accuracy. Specifically, we achieve 72.08% Top-1 accuracy on ResNet-18 with 6.7Mb parameters, which does not require any searching iterations. Given the high efficiency and low data dependency of our algorithm, we use it for the post-training quantization, which achieves 71.27% Top-1 accuracy on MobileNetV2 with only 1.5Mb parameters.

Evaluation Of Drill Bit Selection On Basement Layer Exploration Drilling In AFM Block

In one of the exploration drilling of the AF-01 well, there was a drilling problem on the 8 in hole when it penetrated the basement layer. ROP at the time of drilling the basement layer is far below the estimated ROP.By setting MSE as, ROP is increased so that cost savings are obtained from reducing rig time. A qualitative analysis of drillbit selection was carried out based on the type and design of the drillbit as well as the characteristics of the basement rock to be penetrated.In addition to the qualitative and quantitative assessment was carried out through the calculation of the PDC Design Index, MSE, DSE and CPF on each drill bit.ROP of Hybrid PDC-1 increased 500% when compared to PDC-1. The MSE and DSE produced by the Hybrid PDC-1 are 750% smaller than the PDC-1. Using the Hybrid PDC-1, drilling costs can be reduced up to USD 2,105,481.The low ROP at the time of drilling the basement layer in well AF-01 was due to the incompatibility of the PDC-1 bit used for the type of rock being penetrated. Hybrid PDC-1 is the most optimum bit to penetrate the basement layer in the AFM Block.

Deep learning based sequence detection with natural redundancy for memory sources

AbstractThe current wireless communication system has higher performance demands for receivers. This paper considers exploiting the natural redundancy (NR) that is widely existed in the transmission sources to improve the receiver's performance of sequence detection. The type of NR discussed in this paper is the inherent redundancy in data caused by the correlation between symbols in the memory sources. Since the correlation between transmitted symbols is too challenging to obtain, deep learning (DL) is introduced into sequence detection, which has a powerful ability to extract complex patterns from data. Specifically, intended for a specific memory source that is convenient for performance analysis, namely the Markov source, an iterative sequence detection algorithm based on the long short‐term memory network is proposed. Simulation results demonstrate that the receiver's performance can be improved by exploiting the NR and the proposed DL‐based sequence detection scheme can obtain optimal bit error rate performance with blind state transition probability of the transmitted Markov source.

High-level synthesis assisted, low-latency, area- and power-optimized FPGA implementation of MUSIC algorithm for direction-of-arrival estimation

For multiple signal-processing applications, estimating the target’s speed, distance, and elevation angle by using applications running in real time and consequently, fast data rates is important. Custom hardwares, such as field programmable gate arrays (FPGAs), are often used to prototype and deploy such signal-processing algorithms. Multiple signal classification (MUSIC) is typically used for direction-of-arrival (DOA) estimation. It involves complex mathematical operations such as covariance matrix computation, eigen value decomposition, and peak value search for signal values. With a novel high-level synthesis (HLS)-based design approach that optimizes the bit widths for the eigen value decomposition logic, this study presents an area and speed-optimal implementation of MUSIC algorithm on FPGA. The final implementation involving the covariance matrix computation, eigen value decomposition, and peak search was implemented on Xilinx Zynq (XC7Z020) FPGA. Because of its optimum bit widths, area utilization was minimized and operation frequency on the target FPGA was maximized. The proposed design works in real time to estimate the DOA in less than 1.7 µs (15 % faster than reported values) and uses up to 30 % less resources on the target FPGA compared with other reported implementations.

Information-Based Bit Allocation for Cooperative Visual Sensing in Vehicular Networks

Cooperative visual sensing among multiple vehicles has raised increasing concern with the development of connected vehicular systems. However, it remains challenging for effective cooperative sensing under communication constraints due to the lack of theoretic basis and efficient design for communication resource allocation in visual sensing networks. This paper establishes an information-based bit allocation framework for cooperative visual sensing in vehicular networks, where visual observations captured by on-board cameras are quantized before transmitted to the fusion center for 3D environment model estimation. Our aim is to allocate the quantization bits among observations in the network to minimize the overall estimation error. Specifically, we first design a Fisher information matrix (FIM)-based metric to evaluate the cooperative visual sensing error, and characterize the impact of quantization on sensing with geometric interpretations based on the relation among the 3D physical space, the quantization modulation space and the information eigenspace. Then we present closed-form investigations for the optimal bit allocation among observations in the network, and derive its dependence on network topology and communication constraints. Moreover, we propose a hierarchical bit allocation scheme and a convex relaxation-based scheme for cooperative visual sensing, and compare their performance with the generalized Breiman, Friedman, Olshen, and Stone (GBFOS) algorithm and the uniform allocation scheme. Numerical results validate that our schemes achieve near-optimal sensing performance under communication constraints with a significant reduction of computation load.

Convolutional neural network-based optimal R-λ intra rate control in Versatile Video Coding

Since intra frame rate control has no prior information to rely on, it is difficult to obtain suitable initial rate control parameters for achieving optimal intra coding. The optimal bit allocation and λ decision are proposed based on a convolutional neural network (CNN) in R-λ intra frame rate control. First, according to the rate distortion (R-D) and rate lambda (R-λ) relationships of the coding tree unit (CTU), a CNN with four outputs is designed to predict the key parameters of R-D and R-λ curves. Second, with the R-D optimization of a frame, the coding optimization equation of the frame λ and the target bit can be built to derive the optimal CTU bit allocation. Last, a suitable CTU λ can be obtained based on the allocated bit and predicted R-λ curve for a CTU to minimize the total distortion of the current frame. The experimental results show that the proposed algorithm can improve the intra frame rate control performance by 0.31 dB with a good rate control accuracy of 4.76%.

Multiple-symbol noncoherent learning detection of coded QAM signals in IEEE 802.15.3 Wireless Multi-media Networks

We consider the noncoherent deep learning problem for coded signal detection under the phase noncoherent channels for remote home healthcare applications with high data rate. In particular, a multiple-symbol noncoherent learning detection (MNLD) scheme based on neural networks is proposed for low-density parity-check (LDPC) coded noncoherent quadrature amplitude modulation (QAM) signals in IEEE 802.15.3 Wireless Multi-media Networks. Our derivation shows that extensive operations for the first kind zero-order modified Bessel function is unavoidable for the implementation of the optimal bit log-likelihood ratio (LLR) for decoding in traditional multiple-symbol detection (MSD) scheme. The perfect estimation of the channel state information (CSI), i.e., a priori information about the variance of the additive white Gaussian noise (AWGN), is also required for the receiver. This is clearly not computationally practical for Wireless Multi-media Networks. Consequently, we developed an improved approach based on feed-forward neural networks to accurately calculate the bit LLR. Furthermore, to decrease the generation size of training set and thus increase the training speed of the proposed neural networks, we uniformly quantize the continuous carrier phase offset (CPO), which is random and unknown, into discrete status. Our simulation results verify the learning efficiency of this simplified training-set generation configuration. The decoding convergence is successfully accelerated and much performance gain is finally achieved when compared with traditional decoding using the perfect bit LLR. This is clearly critical for high reliable transmission of home healthcare information.

Quality-driven video streaming for ultra-dense OFDMA heterogeneous networks

The explosive growth of mobile video traffic has brought enormous traffic pressure to the existing bandwidth-limited cellular network. Ultra-dense heterogeneous networks (HetNets) are deemed as one of the most promising 5G technologies for increasing the cellular network capacity. In this paper, we study quality-driven multiuser video streaming algorithm for two-tier orthogonal frequency division multiple access (OFDMA) HetNets. Specifically, we formulate a stochastic optimization problem for the quality-driven multiuser video streaming in two-tier OFDMA HetNets to maximize the average perceived video quality of small cell users while meeting the average data rate demand of macrocell users. We then decompose the original stochastic problem into a sequence of per-slot deterministic subproblems of adaptive bitrate selection and radio resource management which have no requirement of prior channel knowledge by employing Lyapunov optimization technique. We accordingly propose an online quality-driven video streaming algorithm. In this algorithm, we derive the optimal bitrate decision for the adaptive bitrate selection subproblem, and design an iterative scheduling algorithm of joint subchannel assignment and power control by utilizing alternating optimization and successive convex approximation techniques to solve the radio management subproblem. Extensive simulation results validate the high efficiency of the proposed quality-driven video streaming algorithm in OFDMA HetNets.