Bit Rate Reduction Research Articles

Multicarrier dual‐index permuted chaos shift keying system for efficiency improvement in chaotic communications

AbstractWe propose a multicarrier chaotic system that incorporates dual‐index and M‐ary modulation techniques. The proposed system improves the energy and spectrum efficiencies with a tradeoff in computational complexity. In addition, it applies index modulation to select the chaotic signal and permuted chaotic signals along with ‐ary modulated symbols. As a result, the spectral and energy efficiencies are increased in chaotic systems. Theoretical expressions are derived to calculate the bit error rate of the proposed system under additive white Gaussian noise and Rayleigh fading channels. The equations are validated through Monte Carlo simulations, whose results demonstrate a reduction in bit error rate for the proposed system. The data rate, spectral efficiency, energy efficiency, and computational complexity of the proposed system are analyzed and compared with those of existing multicarrier chaotic systems.

Reinforcement learning-based automated modulation switching algorithm for an enhanced underwater acoustic communication

Acoustic communication is the preferred method for underwater communication due to its superior propagation characteristics, including longer range, lower attenuation, better penetration, and adaptability to water density. However, challenges like varying water depths, temperature gradients, noise, and multipath propagation require innovative solutions beyond fixed-data rate systems and single modulation schemes. To address these challenges, we propose a reinforcement learning (RL)-based automated modulation switching algorithm designed to enhance data transmission efficiency. This approach leverages the UNETStack software and the Frequency Hopping-Binary Frequency Shift Key (FH-BFSK) modulation, extending its capabilities by incorporating Amplitude Shift Keying (ASK), Phase Shift Keying (PSK), and Orthogonal Frequency Division Multiplexing (OFDM) modulation techniques. We designed a cost-effective, software-controlled acoustic modem using commercial off-the-shelf (COTS) components, enabling full-duplex underwater communication. The RL algorithm utilizes a Q-matrix guided by a greedy policy to assess network conditions, such as data volume and data rates. It continuously monitors underwater environments to select the most suitable modulation scheme dynamically. Experimental validation demonstrates a 3.648 % improvement in Received Signal Strength Indicator (RSSI), a 32 % reduction in bit error rate at a constant 7 dB Signal-to-Noise Ratio (SNR), and a 5 % increase in utility at 10 dB SNR when using OFDM selected by the reinforcement learning algorithm, compared to FH-BFSK.

Opto-electronic machine learning network for Kramers-Kronig receiver linearization.

We numerically demonstrate the use of an opto-electronic network comprising a photonic reservoir and an electronic feedforward equalizer (FFE) to linearize a Kramers-Kronig (KK) receiver. The KK receiver is operated under stringent conditions, with restricted sampling rates and low carrier powers, resulting in a nonlinear behavior. We propose two different network configurations, varying in the placement of the FFE component, and evaluate their ability to linearize the KK receiver. By training these networks on back-to-back systems, we arrive at a generic solution that significantly enhances the receiver performance, independent of specific link characteristics. The trained networks are tested in a plug-and-play manner across diverse short-reach links that employ standard digital signal processing blocks for the qualization. Our results show significant improvement in receiver linearity, resulting in a reduction in bit error rate of up to a factor of four.

Performance and energy efficiency enhancement of existing optical communication systems by incorporating resource allocation on demand technique in FiWi networks

Abstract Fiber-wireless (FiWi) networks have begun to be fully integrated into contemporary optical access network systems as a result of utilizing the combined advantages of wired and wireless techniques. Passive optical networks (PON) are heavy energy consumers and one of the main contributors to greenhouse gas emissions (GHG) that are a result of climate change. These systems also present economic challenges. In this work, three different FiWi systems are presented such as (i) even/odd active transmitters with only free space optical (FSO) based optical network units (ONUs)/with dual channel option such as distribution fiber (DF)/FSO supported ONUs, (ii) a novel resource allocation on demand (RAoD) incorporated bidirectional FiWi system supporting DF/FSO based ONUs and four wave mixing (FWM) generated upstream wavelengths and (iii) transmitter diversity enabled 5G supported FiWi system supporting bidirectional communication. It is observed that the energy efficiency of 26.04 % is obtained in even/odd active transmitter and only FSO supported ONUs and under the harsh weather conditions, by adopting DF channel instead of FSO, 30 % energy efficiency further added. However, in novel RAoD technique, energy efficiencies for optical distribution networks (ODN1), ODN2, ODN3, and ODN4 are 48.52 %, 46.4 %, 36.96 %, and 44.31 %. Further, DF option in channel selection improve 30 % more energy saving. In performance enhanced transmitter diversity employed system, at 60 km reduction in bit error rate (BER) are 25 % for ODN1, 5.88 % for ODN2, 20 % for ODN3, and 12.5 % for ODN3.

EFFICIENT VIDEO COMPRESSION USING HEVC AND NON-LINEAR CONVOLUTIONAL MOBILEVNET BASED RATE-DISTORTION OPTIMIZATION

With the surge in demand for high-quality video content over various platforms, efficient video compression techniques have become indispensable. High-Efficiency Video Coding (HEVC) has been a cornerstone, yet further enhancements are essential for optimal compression. Despite HEVC’s advancements, achieving optimal compression while maintaining video quality remains challenging. Additionally, existing methods often overlook the computational complexity, hindering real-time applications. We propose a novel approach integrating HEVC with Non-Linear Convolutional MobileNet (NLCM) for enhanced compression efficiency. Our method employs a rate-distortion optimization framework, leveraging the capabilities of both HEVC and NLCM to achieve superior compression performance. NLCM provides adaptive filtering, enhancing spatial and temporal correlations, while HEVC ensures high compression efficiency. Through experimentation on standard video datasets, our method demonstrates significant improvements over existing techniques. Compared to HEVC alone, our approach achieves up to 30% reduction in bitrate at equivalent perceptual quality levels. Moreover, computational complexity is reduced by 15%, enabling real- time applications without compromising performance. The proposed method exhibits competitive results across various resolutions and frame rates, making it versatile for diverse video compression scenarios.

Optical communication enhanced IDMBOC for maximizing backhaul-effect & maintaining optimum cell sizes

Abstract The increasing need for high data rates and low latency in optical communication networks necessitates innovative solution for system efficiency enhancements. With the persistent increase in data demand and the emergence of diverse applications in 5G networks, minimizing the backhaul-effect while maintaining optimal cell sizes has become a formidable obstacle. Existing methods are incapable of achieving a comprehensive optimization of network parameters, resulting in degraded performance metrics. To address these constraints, our proposed approach integrates optical communication infrastructure into IDMBOC systems which maximizes backhaul-effect and preserves optimal cell sizes. This work is primarily motivated by the need to improve the efficiency and quality of 5G networks in the face of rising data traffic. Existing methods frequently struggle to optimize concurrently multiple 5G network aspects, such as carrier aggregation, dynamic spectrum sharing, packet prioritization, network function virtualization, frequency planning, HetNets deployments, and network slicing process. As a result, these methods are incapable of delivering robust and scalable solutions. To solve these issues, we present an Iterative dual metaheuristic method that combines ant lion optimization (ALO) and grey wolf optimization (GWO) in a synergistic manner for 5G deployments. The proposed method is functionally superior to existing models. By capitalizing on the strengths of both ALO and GWO, our approach achieves superior performance metrics in comparison to recently proposed methods for maximizing backhaul-effect and maintaining optimal cell sizes. The preliminary results reveal a remarkable 8.3 % reduction in bit error rate (BER), 4.9 % reduction in energy consumption, 8.5 % increase in throughput, and 4.5 % reduction in communication delay. The achieved results demonstrate the revolutionary potential of our 5G network optimization approach and pave the way for future research and advancements in the field for different scenarios. These enhancements will revolutionize optical communication networks in order to accommodate the requirements of 5G, IoT, and other contemporary applications.

Block Partitioning Information-Based CNN Post-Filtering for EVC Baseline Profile.

The need for efficient video coding technology is more important than ever in the current scenario where video applications are increasing worldwide, and Internet of Things (IoT) devices are becoming widespread. In this context, it is necessary to carefully review the recently completed MPEG-5 Essential Video Coding (EVC) standard because the EVC Baseline profile is customized to meet the specific requirements needed to process IoT video data in terms of low complexity. Nevertheless, the EVC Baseline profile has a notable disadvantage. Since it is a codec composed only of simple tools developed over 20 years, it tends to represent numerous coding artifacts. In particular, the presence of blocking artifacts at the block boundary is regarded as a critical issue that must be addressed. To address this, this paper proposes a post-filter using a block partitioning information-based Convolutional Neural Network (CNN). The proposed method in the experimental results objectively shows an approximately 0.57 dB for All-Intra (AI) and 0.37 dB for Low-Delay (LD) improvements in each configuration by the proposed method when compared to the pre-post-filter video, and the enhanced PSNR results in an overall bitrate reduction of 11.62% for AI and 10.91% for LD in the Luma and Chroma components, respectively. Due to the huge improvement in the PSNR, the proposed method significantly improved the visual quality subjectively, particularly in blocking artifacts at the coding block boundary.

Learned Focused Plenoptic Image Compression With Microimage Preprocessing and Global Attention

Focused plenoptic cameras can record spatial and angular information of the light field (LF) simultaneously with higher spatial resolution relative to traditional plenoptic cameras, which facilitate various applications in computer vision. However, the existing plenoptic image compression methods present ineffectiveness to the captured images due to the complex micro-textures generated by the microlens relay imaging and long-distance correlations among the microimages. In this paper, a lossy end-to-end learning architecture is proposed to compress the focused plenoptic images efficiently. First, a data preprocessing scheme is designed according to the imaging principle to remove the sub-aperture image ineffective pixels in the recorded light field and align the microimages to the rectangular grid. Then, the global attention module with large receptive field is proposed to capture the global correlation among the feature maps using pixel-wise vector attention computed in the resampling process. Also, a new image dataset consisting of 1910 focused plenoptic images with content and depth diversity is built to benefit training and testing. Extensive experimental evaluations demonstrate the effectiveness of the proposed approach. It outperforms intra coding of HEVC and VVC by an average of 62.57% and 51.67% bitrate reduction on the 20 preprocessed focused plenoptic images, respectively. Also, it achieves 18.73% bitrate saving and generates perceptually pleasant reconstructions compared to the state-of-the-art end-to-end image compression methods, which benefits the applications of focused plenoptic cameras greatly. The dataset and code are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/VincentChandelier/GACN</uri> .

JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer.

JPEG compression adopts the quantization of Discrete Cosine Transform (DCT) coefficients for effective bit-rate reduction, whilst the quantization could lead to a significant loss of important image details. Recovering compressed JPEG images in the frequency domain has recently garnered increasing interest, complementing the multitude of restoration techniques established in the pixel domain. However, existing DCT domain methods typically suffer from limited effectiveness in handling a wide range of compression quality factors or fall short in recovering sparse quantized coefficients and the components across different colorspaces. To address these challenges, we propose a DCT domain spatial-frequential Transformer, namely DCTransformer, for JPEG quantized coefficient recovery. Specifically, a dual-branch architecture is designed to capture both spatial and frequential correlations within the collocated DCT coefficients. Moreover, we incorporate the operation of quantization matrix embedding, which effectively allows our single model to handle a wide range of quality factors, and a luminance-chrominance alignment head that produces a unified feature map to align different-sized luminance and chrominance components. Our proposed DCTransformer outperforms the current state-of-the-art JPEG artifact removal techniques, as demonstrated by our extensive experiments.

Tip and tilt-corrected bit error rate improvement of M-ary pulse position-modulated optical wireless communication in the marine atmosphere

Tip and tilt corrections are applied to the bit error rate of an M-ary pulse position-modulated optical wireless communication system operating in a turbulent marine atmosphere. For the collimated Gaussian laser beam, using the average received power and power scintillation index at the receiver, the reduction in bit error rate is evaluated. Reduction in bit error rate is defined by the metric of bit error rate with tip and tilt correction normalized by the bit error rate without tip and tilt correction. Reduction in bit error rate is found against the changes in average current gain of the avalanche photodiode, data bit rate, and the M value for various structure constant, link length, and wavelength values. Our results show that the application of tip and tilt corrections effectively reduces the bit error rate of the M-ary pulse position-modulated optical wireless communication system in a turbulent marine atmosphere.

On Content-Aware Post-Processing: Adapting Statistically Learned Models to Dynamic Content

Learning-based post-processing methods generally produce neural models that are statistically optimal on their training datasets. These models, however, neglect intrinsic variations of local video content and may fail to process unseen content. To address this issue, this article proposes a content-aware approach for the post-processing of compressed videos. We develop a backbone network, calledBackboneFormer, where a Fast Transformer using Separable Self-Attention, Spatial Attention, and Channel Attention is devised to support underlying feature embedding and aggregation. Furthermore, we introduce Meta-learning to strengthen BackboneFormer for better performance. Specifically, we propose Meta Post-Processing (Meta-PP) which leverages the Meta-learning framework to drive BackboneFormer to capture and analyze input video variations for spontaneous updating. Since the original frame is unavailable to the decoder, we devise a Compression Degradation Estimation model where a low-complexity neural model and classic operators are used collaboratively to estimate the compression distortion. The estimated distortion is then utilized to guide the BackboneFormer model for dynamic updating of weighting parameters. Experimental results demonstrate that the proposed BackboneFormer itself gains about 3.61% Bjøntegaard delta bit-rate reduction over Versatile Video Coding in the post-processing task and “BackboneFormer + Meta-PP” attains 4.32%, costing only 50K and 61K parameters, respectively. The computational complexity of MACs is 49k/pixel and 50k/pixel, which represents only about 16% of state-of-the-art methods having similar coding gains.

Multi-stage affine motion estimation fast algorithm for versatile video coding using decision tree

Affine motion estimation (AME) which is newly introduced in versatile video coding (VVC) plays a significant role in the bit-rate reduction for rotation and zooming scenes. However, it brings the complexity extremely increased. In this paper, a fast AME algorithm is designed to cope with the high-complexity problem. Three crucial stages are involved in the fast algorithm, which is the optimal inter-mode decision based on the coding unit (CU) partition, the fast algorithm in affine motion search (AMS), and the fast inter-mode decision based on a decision tree. Specifically, the optimal inter-mode will be determined straightforwardly when the parent CU is checked whether it is translational motion estimation (TME). Then, three fast algorithms are made in AMS, which are early termination based on control point motion vector (CPMV), early termination of the iteration process, and fast fine granularity CPMV search. Finally, the optimal inter-mode is predicted using a decision tree based on eight essential features. Experimental results show that the proposed algorithm achieves 10.20% and 10.33% encoding time-saving on average under Low Delay B (LDB) and Random Access (RA) configuration, while the BD-Rate loss is only 0.12% and 0.14%, respectively.

Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model

In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep neural networks to learn point cloud geometry and color probability distributions. Our method represents a point cloud with both occupancy feature and three attribute features at different bit depths in a unified sparse representation. This allows us to efficiently exploit feature-wise and point-wise dependencies within point clouds using a sparse tensor-based neural network and thus build an accurate auto-regressive context model for an arithmetic coder. To the best of our knowledge, this is the first learning-based lossless point cloud geometry and attribute compression approach. Compared with the-state-of-the-art lossless point cloud compression method from Moving Picture Experts Group (MPEG), our method achieves 22.6% reduction in total bitrate on a diverse set of test point clouds while having 49.0% and 18.3% rate reduction on geometry and color attribute component, respectively.

Video Compression using Neural Weight Step and Huffman Coding Techniques

Video Compression using Neural Weight Step and Huffman Coding Techniques

A new time–frequency representation based on the tight framelet packet for telephone-band speech coding

To improve the quality and intelligibility of telephone-band speech coding, a new time–frequency representation based on a tight framelet packet transform is proposed in this paper. In the context of speech coding, the effectiveness of this representation stems from its resilience to quantization noise, and reconstruction stability. Moreover, it offers a sub-band decomposition and good time–frequency localization according to the critical bands of the human ear. The coded signal is obtained using dynamic bit allocation and optimal quantization of normalized framelet coefficients. The performances of the corresponding method are compared to the critically sampled wavelet packet transform. Extensive simulation revealed that the proposed speech coding scheme, which incorporates the tight framelet packet transform performs better than that based on the critically sampled wavelet packet transform. Furthermore, it ensures a high bit-rate reduction with negligible degradation in speech quality. The proposed coder is found to outperform the standard telephone-band speech coders in term of objective measures and subjective evaluations including a formal listening test. The subjective quality of our codec at 4 kbps is almost identical to the reference G.711 codec operating at 64 kbps.

Adaptive Fuzzy Logic Deep-Learning Equalizer for Mitigating Linear and Nonlinear Distortions in Underwater Visible Light Communication Systems

Underwater visible light communication (UVLC) has recently come to light as a viable wireless carrier for signal transmission in risky, uncharted, and delicate aquatic environments like seas. Despite the potential of UVLC as a green, clean, and safe alternative to conventional communication methods, it is challenged by significant signal attenuation and turbulent channel conditions compared to long-distance terrestrial communication. To address linear and nonlinear impairments in UVLC systems, this paper presents an adaptive fuzzy logic deep-learning equalizer (AFL-DLE) for 64 Quadrature Amplitude Modulation-Component minimal Amplitude Phase shift (QAM-CAP)-modulated UVLC systems. The proposed AFL-DLE is dependent on complex-valued neural networks and constellation partitioning schemes and utilizes the Enhanced Chaotic Sparrow Search Optimization Algorithm (ECSSOA) to improve overall system performance. Experimental outcomes demonstrate that the suggested equalizer achieves significant reductions in bit error rate (55%), distortion rate (45%), computational complexity (48%), and computation cost (75%) while maintaining a high transmission rate (99%). This approach enables the development of high-speed UVLC systems capable of processing data online, thereby advancing state-of-the-art underwater communication.

Real-World Use of AI for Better Video Compression

Video compression is a complex task, with an almost unlimited number of potential encoding decision permutations. While software-based encoders can, in theory, exhaustively explore all the possible encoding options in a bid to maximize compression efficiency, such an approach means there is the potential to consume as much central processing unit (CPU) power as is available. A direct consequence is a poor tradeoff between the processing required and the reduction in bitrate achieved, especially since technical and commercial limitations often place constraints on the amount of processing for any given application. A long-standing aim for encoders is, therefore, to optimize the use of the available computing resources, either by driving down the bitrates (and therefore delivery costs) or reducing the CPU cost to achieve the required quality. A common approach to reduce CPU costs is to take shortcuts and make decisions upfront based on less information, in an attempt to guess or shortlist decisions without exhaustively evaluating all options. However, the diverse and extensive number of permutations of options available in modern codecs make these decisions very complex, meaning that this approach normally leads to a less bitrate-efficient compression. If means can be found to utilize the CPU more effectively, then the objective of improved encoder performance, measured in either bitrate reduction, infrastructure cost, or a combination of both, can be achieved. This is the application space considered in this article: how to use artificial intelligence (AI) and machine learning (ML) to efficiently perform encoding decisions in realtime based on the characteristics of the incoming content, allowing the use of the CPU power available more effectively (or equivalently, reducing the need for CPU power). Considering the AI processing itself also requires CPU power, such an application requires carefully tuned approaches to guarantee that the combination of the AI processing plus its dynamically adapted encode algorithm achieves a better bitrate versus CPU for the same quality than can be achieved using standard algorithms.

Overview of Versatile Video Coding (H.266/VVC) and Its Coding Performance Analysis

Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). H.266/VVC provides about 40% bitrate reduction compared with High Efficiency Video Coding (H.265/HEVC) for the same visual quality. This paper introduces in detail the core structure of H.266/VVC by highlighting its features within block partitioning structure, intra/inter prediction, transform, quantization, and in-loop filtering, compared to its predecessor (H.265/HEVC). H.266/VVC yields significantly improved the coding performance, but it increased the computational complexity, particularly for the encoder side, which remains a problem to be tackled for successful commercialization in the future. This paper examined the statistical performance of H.266/VVC coding tools from the bitstreams encoded by the VVC test model (VTM12.0) encoder through rate-distortion optimization under the JVET common test conditions. In addition, the complexity and performance analyses are conducted on the block partitioning structure and group of picture structure. It is expected that an optimized H.266/VVC encoder can be designed and developed by minimizing the coding loss based on the analysis data.

Low calculation cost of HEVC coding unit size based on spatial homogeneity detection

For the same video quality, HEVC gives 25% to 50% bitrate savings, compared to its predecessor the Advanced Video Coding H.264 and thus supports resolutions up to 8 K UHD. However, the reduction in bitrates provided by the HEVC leads to an increase in the computational cost of the encoding operation. This complexity can become a true handicap especially for real-time video streaming and also for VANET (Vehicular Ad-Hoc Network) applications such as traffic safety and Video surveillance. The improvement in the bitrates and also the increase in the calculation cost are due to the use of large and multi-sized coding, prediction and transform blocks. Indeed, the H264 coder is based on structure macroblocks with sizes 4 × 4, 8 × 8 and 16 × 16, while H.265 depends on Coding Tree Units (CTUs), CTUs select sizes 4 × 4, 8 × 8, 16 × 16, 32 × 32 and 64 × 64 blocks. This paper proposes a fast CU (Coding Unit) size decision method to reduce the HEVC calculation cost based on spatial homogeneity. Compared with the HM16.13 benchmark test model, the average coding time is reduced by around 40% for CIF / QCIF video sequences, 35% to 43% for class A, B and C test sequences. These important reductions in coding time are obtained with negligible loss of quality and an average increase in bitrates which does not exceed 0.89% for the three configuration modes (All intra, Random Access and Low Delay).

Reduced Complexity Neural Network Equalizers for Two-Dimensional Magnetic Recording

This article investigates the reduced complexity neural network (NN)-based architectures for equalization over the two-dimensional magnetic recording (TDMR) digital communication channel for data storage. We use realistic waveforms measured from a hard disk drive (HDD) with TDMR technology. We show that the multilayer perceptron (MLP) nonlinear equalizer achieves a 10.91% reduction in bit error rate (BER) over the linear equalizer with cross-entropy (CE)-based optimization. However, the MLP equalizer’s complexity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.6\times $ </tex-math></inline-formula> the linear equalizer’s complexity. Thus, we propose the reduced complexity MLP (RC-MLP) equalizers. Each RC-MLP variant consists of finite-impulse response (FIR) filters, a nonlinear activation, and a hidden delay line. A proposed RC-MLP variant entails only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.59\times $ </tex-math></inline-formula> the linear equalizer’s complexity while achieving a 8.23% reduction in BER over the linear equalizer.