Adaptive Quantization Research Articles

Adaptive QP algorithm for depth range prediction and encoding output in virtual reality video encoding process.

In order to reduce the encoding complexity and stream size, improve the encoding performance and further improve the compression performance, the depth prediction partition encoding is studied in this paper. In terms of pattern selection strategy, optimization analysis is carried out based on fast strategic decision-making methods to ensure the comprehensiveness of data processing. In the design of adaptive strategies, different adaptive quantization parameter adjustment strategies are adopted for the equatorial and polar regions by considering the different levels of user attention in 360 degree virtual reality videos. The purpose is to achieve the optimal balance between distortion and stream size, thereby managing the output stream size while maintaining video quality. The results showed that this strategy achieved a maximum reduction of 2.92% in bit rate and an average reduction of 1.76%. The average coding time could be saved by 39.28%, and the average reconstruction quality was 0.043, with almost no quality loss detected by the audience. At the same time, the model demonstrated excellent performance in sequences of 4K, 6K, and 8K. The proposed deep partitioning adaptive strategy has significant improvements in video encoding quality and efficiency, which can improve encoding efficiency while ensuring video quality.

Impact of Wireless Network Packet Loss on Real-Time Video Streaming Application: A Comparative Study of H.265 and H.266 Codecs

The transmission of real-time videos over wireless networks is prone to the negative consequences of packet loss and delay, which can have a potential effect on the video quality during streaming. These impairments can lead to interruptions, buffering, and degradation of visual and auditory elements, resulting in an unsatisfactory user experience. In this paper, we aim to address the challenges associated with packet loss and delay parameters in wireless networks, and propose an approach to alleviate their impact on real-time video transmission. The proposed approach involves utilizing the H.265/H.266 video coding standards. For Versatile Video Coding (VVC), a patch support for VVdeC and VVenC to FFmpeg (Fast Forward Moving Picture Expert Group) is added. As a result, FFmpeg is used to encode, stream and decode all videos. Raw videos of 2K qualities are encoded based on the adaptive quantization (QP) for the above-mentioned codecs. By selecting optimal transmission data based on various network conditions, this approach enhances the Quality of Experience (QoE) for end-users while minimizing resource usage in the wireless network. Furthermore, the proposed approach selects the codec standards according to their bitrates and frame rates. Simulation results indicate that the proposed approach has a significant improvement for real-time video streaming over wireless networks to satisfy the end user experience. The approach also outperforms other related work by gaining a PSNR of +12 dB for H.265 and +13 dB for H.266 when the network packet loss is 1%.

Efficient On-Board Compression for Arbitrary-Shaped Cloud-Covered Remote Sensing Images via Adaptive Filling and Controllable Quantization

Due to the fact that invalid cloud-covered regions in remote sensing images consume a considerable quantity of coding bit rates under the limited satellite-to-ground transmission rate, existing image compression methods suffer from low compression efficiency and poor reconstruction quality, especially in cloud-free regions which are generally regarded as regions of interest (ROIs). Therefore, we propose an efficient on-board compression method for remote sensing images with arbitrary-shaped clouds by leveraging the characteristics of cloudy images. Firstly, we introduce two novel spatial preprocessing strategies, namely, the optimized adaptive filling (OAF) strategy and the controllable quantization (CQ) strategy. Specifically, the OAF strategy fills each cloudy region using the contextual information at its inner and outer edge to completely remove the information of cloudy regions and minimize their coding consumption, which is suitable for images with only thick clouds. The CQ strategy implicitly identifies thin and thick clouds and rationally quantifies the data in cloudy regions to alleviate information loss in thin cloud-covered regions, which can achieve the balance between coding efficiency and reconstructed image quality and is more suitable for images containing thin clouds. Secondly, we develop an efficient coding method for a binary cloud mask to effectively save the bit rate of the side information. Our method provides the flexibility for users to choose the desired preprocessing strategy as needed and can be embedded into existing compression framework such as JPEG2000. Experimental results on the GF-1 dataset show that our method effectively reduces the coding consumption of invalid cloud-covered regions and significantly improve the compression efficiency as well as the quality of decoded images.

Entropy‐driven Progressive Compression of 3D Point Clouds

Abstract3D point clouds stand as one of the prevalent representations for 3D data, offering the advantage of closely aligning with sensing technologies and providing an unbiased representation of a measured physical scene. Progressive compression is required for real‐world applications operating on networked infrastructures with restricted or variable bandwidth. We contribute a novel approach that leverages a recursive binary space partition, where the partitioning planes are not necessarily axis‐aligned and optimized via an entropy criterion. The planes are encoded via a novel adaptive quantization method combined with prediction. The input 3D point cloud is encoded as an interlaced stream of partitioning planes and number of points in the cells of the partition. Compared to previous work, the added value is an improved rate‐distortion performance, especially for very low bitrates. The latter are critical for interactive navigation of large 3D point clouds on heterogeneous networked infrastructures.

RawHash2: mapping raw nanopore signals using hash-based seeding and adaptive quantization.

Raw nanopore signals can be analyzed while they are being generated, a process known as real-time analysis. Real-time analysis of raw signals is essential to utilize the unique features that nanopore sequencing provides, enabling the early stopping of the sequencing of a read or the entire sequencing run based on the analysis. The state-of-the-art mechanism, RawHash, offers the first hash-based efficient and accurate similarity identification between raw signals and a reference genome by quickly matching their hash values. In this work, we introduce RawHash2, which provides major improvements over RawHash, including more sensitive quantization and chaining algorithms, weighted mapping decisions, frequency filters to reduce ambiguous seed hits, minimizers for hash-based sketching, and support for the R10.4 flow cell version and POD5 and SLOW5 file formats. Compared to RawHash, RawHash2 provides better F1 accuracy (on average by 10.57% and up to 20.25%) and better throughput (on average by 4.0× and up to 9.9×) than RawHash. RawHash2 is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.

Distributed Online Optimization Under Dynamic Adaptive Quantization

Distributed Online Optimization Under Dynamic Adaptive Quantization

Adaptive non-iterative histogram-based hologram quantization

Quantization is widely used for compression, storage, transmission and display of computer-generated and digital holograms. It reduces number of hologram gradations to obtain better ratio of the quality of reconstructed 2D and 3D objects to the size of holographic images or videos. This paper proposes 2 methods of non-iterative quantization of holograms: adaptive non-uniform quantization based on histogram processing with peak shift (ANHS method) and additional application of logarithmic companding (ANHSL method). They were compared with 4 methods of iterative and non-iterative quantization on holograms with sizes up to 2048 × 2048 pixels. The proposed methods showed the best reconstruction quality that is higher up to 59 % in comparison with non-iterative uniform quantization. Especially the proposed methods give higher quality at little number of hologram gradations (from 2 to 16 gradations). In comparison with the most qualitative iterative methods (Lloyd-Max and k-means) the quality is increased up to 9 %, and processing time is reduced in 10–1000 times.

JND-based multi-module cooperative perceptual optimization for HEVC

Since the just noticeable distortion (JND) reflects the tolerance limit of human visual system (HVS) to coding distortion directly, JND-based perceptual video coding (PVC) plays an increasingly significant role in video compression with the developing explosion of video data. In this paper, we focus on the coupling effect among coding modules and provide a JND-based multi-module cooperative perceptual optimization (JMCPO) scheme for HEVC. The main contribution of the proposed JMCPO scheme includes the following three aspects. (1) Based on quantization distortion estimation, an adaptive perceptual quantization scheme is proposed using the binary search approach on the premise of that the quantization distortion is infinitely close to the estimated JND threshold. (2) The coupling effect among coding modules is first analyzed and a novel perceptual residual filtering scheme is presented based on statistic analysis of coupling strength. (3) The JMCPO scheme is finally developed through collaborative optimization of residual filtering, quantization and rate–distortion optimization. Experimental results show that the proposed JMCPO scheme saves more bitrate with better subjective and objective qualities.

Adaptive Distributed Heterogeneous Formation Control for UAV-USVs with Input Quantization

This paper investigates the cooperative formation trajectory tracking problem for heterogeneous unmanned aerial vehicle (UAV) and multiple unmanned surface vessel (USV) systems with input quantization performance. Firstly, at the kinematic level, a distributed guidance law based on an extended state observer (ESO) is designed to compensate for the unknown speed of neighbor agents for expected trajectory tracking, and subsequently at the dynamic level, an ESO is utilized to estimate model uncertainties and environmental disturbances. Following that, a linear analytic model is employed to depict the input quantization process, and the corresponding adaptive quantization controller is designed without necessitating prior information on quantization parameters. Based on the input-to-state stability, the stability of the proposed control structure is proved, and all the signals in the closed-loop system are ultimately bounded. Finally, a simulation study is provided to show the efficacy of the proposed strategy.

AQUILA: Communication Efficient Federated Learning With Adaptive Quantization in Device Selection Strategy

AQUILA: Communication Efficient Federated Learning With Adaptive Quantization in Device Selection Strategy

Observer-based fuzzy control for fractional order PMSG wind turbine systems with adaptive quantized-mechanism

This study aims to present an observer-based fuzzy control approach for a wind turbine system (WTS) equipped with a fractional-order permanent magnet synchronous generator (PMSG) by utilizing an adaptive quantized-mechanism. To do this, firstly, the fractional order control is introduced into the nonlinear PMSG model to improve the convergence rate beyond the existing integer order control techniques. In the next step, the nonlinear PMSG model is converted into linear sub-models using Takagi–Sugeno (T–S) fuzzy models. Using a fractional order Lyapunov function, an adaptive quantization mechanism, and a linear matrix inequality (LMI), we then obtain the necessary conditions for global stabilization of the PMSG-based WTS. Finally, the method proposed in this study demonstrates its superiority and efficiency through comparison with existing methods.

Design and analysis of stochastic 5G new radio LDPC decoder using adaptive sparse quantization kernel least mean square algorithm for optical satellite communications

AbstractA Stochastic Low‐Density Parity‐Check (LDPC) decoder is a type of 5G New Radio standard LDPC decoder that uses stochastic techniques to perform decoding. Stochastic LDPC decoding with 5G NR standard typically uses an iterative process, where messages exchanged among variable nodes (VN), check nodes multiple times. Stochastic LDPC decoders are often used in scenarios where the received signal is subject to varying levels of noise. They will provide improved error correction performance compared to traditional LDPC decoders, especially when dealing with channels with varying signal‐to‐noise ratios in 5G networks. Using the adaptive sparse quantization kernel least mean square algorithm (SLDPC‐ASQ‐KLMSA), this paper proposes an area‐efficient architecture design for a stochastic LDPC decoder. The LDPC code (2048, 1723) is taken from the LOGBASE‐T standard and used in this study. We examine the ASQ‐KLMSA connection effects. Starting with the VN. It makes checking node functioning easier and reduces inter‐connect complexity by capping extrinsic message length at 2 bits. Because of the simplified check node operation in ASQ‐KLMSA, the decoder nodes must exchange messages with a greater degree of accuracy. The 3–3 input grouping sub‐node of the degree‐6 VN was changed with an adder‐based 5–1 input grouping sub‐node for the (2048, 1723) code in order to get more accurate results when the check‐to‐variable messages aren't strong enough. A suggested decoder architecture was determined using a stochastic LDPC decoder developed for TSMC 65 nm process (2048, 1723). Bite error rate, throughput, mean square error, latency, power, and area usage are some of the metrics used to evaluate the effectiveness of the SLDPC‐ASQ‐KLMSA algorithm that has been suggested and implemented in Python. Thus, the proposed approach has attained 34.44%, and 38.39% low mean square error while compared with the existing methods such as higher‐performance stochastic LDPC decoder architecture designed through correlation analysis (HP‐SLDPC‐CA), Higher Throughput and Hardware Efficient Hybrid LDPC Decoder Utilizing Bit‐Serial Stochastic Updating(HLDPC‐BSSU), Flexible FPGA‐Based Stochastic Decoder for 5G LDPC codes (FPGA‐SD‐5G‐LDPC), respectively.

Cracks-suppression perceptual geometry coding for dynamic point clouds

Dynamic point clouds can effectively describe 3D objects and natural scenes, providing users with an immersive visual experience, but their huge amount of data requires efficient compression tools. To this end, the video-based point cloud compression (V-PCC) standard was developed, however, it may lead to cracks in the compressed point cloud at low bitrates. To solve the problem, this paper proposes a cracks-suppression perceptual geometry coding method for dynamic point cloud. Firstly, considering that the edge regions of 2D geometry patches in V-PCC are prone to cracks, a prior knowledge based crack region detection and preprocessing scheme is designed to reduce cracks. Secondly, considering the unique perceptual geometry characteristics of point cloud, a projected geometry curvature based structural similarity (PGC-SSIM) is proposed to evaluate geometry quality of point clouds, which contains the geometric perception information of point cloud, and is more consistent with the human visual perception. Finally, based on the PGC-SSIM, an adaptive quantization parameter adjustment strategy is designed for rate-distortion optimization in geometry coding of dynamic point clouds. The experimental results show that the proposed method can effectively reduce cracks in the compressed point cloud without reducing the coding performance compared to the V-PCC anchor. Moreover, the presented PGC-SSIM can be used to improve the visual quality of the compressed point cloud.

The Data Compression Method and FPGA Implementation in the Mars Rover Subsurface-Penetrating Radar on the Tianwen-1 Mission

Since Mars is far away from Earth, the propagation delay between Mars and Earth is very large. To ensure the effective use of the link transmission bandwidth, China’s first Mars exploration mission has put forward a demand for data compression for all scientific payloads. The on-board mature algorithms for data compression are mainly focused on optical images and microwave imaging radar applications. No articles have been published on data compression methods that are applied to subsurface-penetrating radar. Based on the background of this application, this paper proposes a logarithmic lossy compression algorithm which can meet the mission requirements for high compression ratios of 4:1 and 2.5:1. Its compression error is less than that of the block adaptive quantization (BAQ) algorithm. The algorithm is not only easy to implement on field-programmable gate array (FPGA) platforms, but also offers simple ground decompression and fast imaging. The experimental results show that high compression ratios of 4:1 and 2.5:1 can be realized, even if the data in and between traces do not have a strong correlation. And its relative error is less than 2%, which is a new type of high-efficiency data compression method that can be implemented on-board to meet with the demand of subsurface penetrating radar.

Intrusion Detection System for MIL-STD-1553 Based on Convolutional Neural Networks With Binary Images and Adaptive Quantization

Intrusion Detection System for MIL-STD-1553 Based on Convolutional Neural Networks With Binary Images and Adaptive Quantization

Coding–Decoding-Based Sliding Mode Control for Markovian Jump Systems Under Constrained Bit Rate: An Adaptive Quantizer Approach

This paper investigates the asynchronous sliding mode control (SMC) issue for uncertain hidden Markovian jump systems (MJSs) under constrained bit rate. An improved coding-decoding procedure with state-dependent adaptive quantization parameters is established to enable the controller to decode state and mode information from the system and the detector, respectively. Based on this procedure, a decoded-data-based asynchronous SMC strategy is provided and sufficient conditions are developed to ensure that the closed-loop dynamics are exponentially ultimately bounded and the system state is driven onto a sliding region simultaneously in the sense of mean square. The desired parameters of the quantizer and controller are obtained by solving an optimization problem, in which the objective function is constructed by using the trade-off between the ultimate state bound and the decay rate. Moreover, the integrative design of bit rate allocation protocol and quantizer as well as control parameters are converted into a nonlinear programming problem with integer constraint, which is solved by means of an improved sparrow search algorithm. Finally, a direct current motor system is employed to illustrate the effectiveness of the present approach under three different cases.

Novel adaptive quantization methodology for 8-bit floating-point DNN training

There is a high energy cost associated with training Deep Neural Networks (DNNs). Off-chip memory access contributes a major portion to the overall energy consumption. Reduction in the number of off-chip memory transactions can be achieved by quantizing the data words to low data bit-width (E.g., 8-bit). However, low-bit-width data formats suffer from a limited dynamic range, resulting in reduced accuracy. In this paper, a novel 8-bit Floating Point (FP8) data format quantized DNN training methodology is presented, which adapts to the required dynamic range on-the-fly. Our methodology relies on varying the bias values of FP8 format to fit the dynamic range to the required range of DNN parameters and input feature maps. The range fitting during the training is adaptively performed by an online statistical analysis hardware unit without stalling the computation units or its data accesses. Our approach is compatible with any DNN compute cores without any major modifications to the architecture. We propose to integrate the new FP8 quantization unit in the memory controller. The FP32 data from the compute core are converted to FP8 in the memory controller before writing to the DRAM and converted back after reading the data from DRAM. Our results show that the DRAM access energy is reduced by 3.07×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} while using an 8-bit data format instead of using 32-bit. The accuracy loss of the proposed methodology with 8-bit quantized training is ≈1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 1\\%$$\\end{document} for various networks with image and natural language processing datasets.

Distributed Constrained Optimization With Delayed Subgradient Information Over Time-Varying Network Under Adaptive Quantization.

In this article, we consider a distributed constrained optimization problem with delayed subgradient information over the time-varying communication network, where each agent can only communicate with its neighbors and the communication channel has a limited data rate. We propose an adaptive quantization method to address this problem. A mirror descent algorithm with delayed subgradient information is established based on the theory of Bregman divergence. With a non-Euclidean Bregman projection-based scheme, the proposed method essentially generalizes many previous classical Euclidean projection-based distributed algorithms. Through the proposed adaptive quantization method, the optimal value without any quantization error can be obtained. Furthermore, comprehensive analysis on the convergence of the algorithm is carried out and our results show that the optimal convergence rate can be obtained under appropriate conditions. Finally, numerical examples are presented to demonstrate the effectiveness of our results.

Self-Supervised Monocular Depth Estimation With Positional Shift Depth Variance and Adaptive Disparity Quantization.

Recently, attempts to learn the underlying 3D structures of a scene from monocular videos in a fully self-supervised fashion have drawn much attention. One of the most challenging aspects of this task is to handle independently moving objects as they break the rigid-scene assumption. In this paper, we show for the first time that pixel positional information can be exploited to learn SVDE (Single View Depth Estimation) from videos. The proposed moving object (MO) masks, which are induced by the depth variance to shifted positional information (SPI) and are referred to as 'SPIMO' masks, are highly robust and consistently remove independently moving objects from the scenes, allowing for robust and consistent learning of SVDE from videos. Additionally, we introduce a new adaptive quantization scheme that assigns the best per-pixel quantization curve for depth discretization, improving the fine granularity and accuracy of the final aggregated depth maps. Finally, we employ existing boosting techniques in a new way that self-supervises moving object depths further. With these features, our pipeline is robust against moving objects and generalizes well to high-resolution images, even when trained with small patches, yielding state-of-the-art (SOTA) results with four- to eight-fold fewer parameters than the previous SOTA techniques that learn from videos. We present extensive experiments on KITTI and CityScapes that show the effectiveness of our method.

Communication-efficient ADMM using quantization-aware Gaussian process regression

In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Gaussian processes (GPs) are effective at learning the agents' local proximal operators, thereby reducing the communication between the agents and the coordinator. We propose combining this learning method with adaptive uniform quantization for a hybrid approach that can achieve further communication reduction. In our approach, due to data quantization, the GP algorithm is modified to account for the introduced quantization noise statistics. We further improve our approach by introducing an orthogonalization process to the quantizer's input to address the inherent correlation of the input components. We also use dithering to ensure uncorrelation between the quantizer's introduced noise and its input. We propose multiple measures to quantify the trade-off between the communication cost reduction and the optimization solution's accuracy/optimality. Under such metrics, our proposed algorithms can achieve significant communication reduction for distributed optimization with acceptable accuracy, even at low quantization resolutions. This result is demonstrated by simulations of a distributed sharing problem with quadratic cost functions for the agents.