Adaptive Quantization Algorithm Research Articles

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.

FLoRa: Sequential fuzzy extractor based physical layer key generation for LPWAN

The security of Low-Power Wide-Area Network (LPWAN) mainly relies on encryption for ensuring packet integrity and confidentiality. Unfortunately, the latest LPWAN specifications refrain from specifying how to distribute keys for encryption. In this paper, we tackle this problem via physical layer security, which exploits the channel characteristics to generate secret keys at the physical layer. In order to generate consistent keys from noisy feature sources and to achieve high reconciliation success rate, we propose FLoRa, a physical layer key generation system for LPWAN based on sequential fuzzy extractor. An adaptive multi-bit quantization algorithm is first proposed to generate the initial key, which accelerates the bit generation rate at the start-up procedure. We then design a novel fuzzy extractor by sequentially slicing the initial key, which improves the reconciliation success rate, as well as reduces the key reconciliation time. We implement FLoRa in a LoRaWAN based network prototype and evaluate it by conducting extensive indoor and outdoor experiments. Experimental results reveal that FLoRa is capable of generating consistent secret keys with high key generation performance in both static and dynamic network environments.

Physical-Layer Secret Key Generation in Power Line Communication Networks

Due to the inherent broadcast nature, power line communication (PLC) is vulnerable to eavesdropping. Traditionally, the secret keys are generated/distributed by pre-shared keys, adaptive tone mapping, or asymmetric encryption algorithm. All of these methods face problems such as high computational complexity, high cost, or less security. To solve the problem, a physical layer security (PLS)-based secret key generation (SKG) scheme aiming at the typical in-home broadband PLC network is proposed in this paper. However, the slowly time-varying characteristic and the serious noise interference in PLC channels will degrade the performance of the PLS-based SKG scheme. To break through the limitation of the randomness of PLC channels, two-way random signals are introduced. To make the SKG system more resistant to noise, the channel frequency response-based adaptive quantization (CFR-AQ) algorithm is designed, in which the amplitude threshold and the quantization level are adaptive to the noise power and the target key disagreement rate (KDR) to maximize the key generation rate (KGR). The performance is analyzed in two scenarios: single eavesdropper and multiple eavesdroppers. The simulation results demonstrate that the proposed scheme has a good overall performance in terms of KDR, KGR, and security with a lower complexity in the presence of single or multiple non-collusive eavesdroppers, and it can be regarded as an alternative solution to generate secret keys in PLC networks.

Реализация метода адаптивного квантования в измерительных генераторах с цифровым управлением

Measuring generators with digital control, in particular power calibrators, used to calibrate electricity meters, contain a digital-to-analog converter (DAC) that converts codes of the generated signal into voltage. Signal codes are stored in the generator memory. A truncation discreteness error (quantization noise) arises caused by sampling (quantization) in time and by the level of signal samples in the DAC. A relative value of the quantization noise depends on the amplitude of the generated signal (relative to the reference voltage of the DAC): the larger the amplitude, the more significant bits of the DAC are involved in the conversion process, and the less the relative value of the noise. In generators, where the amplitude of the output signal changes over a wide range (high dynamic range) by changing the digital samples of the signal, the quantization noise at low signal amplitudes can become unacceptably large. This situation occurs in power calibrators where the output current changes hundreds of times since the error of the verified electricity meter is normalized in a wide range of current flowing through it. A new algorithm for generating samples of a sinusoidal signal in measuring generators with digital control called adaptive quantization is proposed. Adaptive quantization can significantly improve one of the selected signal parameters (the so-called optimality criterion), for example, reduce the error in reproduction of the first harmonic, or reduce the value of higher harmonic components. In addition, the proposed algorithm reduces the dependence of the selected parameter on the sampling frequency and on the number of DAC bits used, which makes it possible to expand the dynamic range of the generator (in the current channel) without using additional amplifiers with programmable gain (PGA). Studies carried out using computer simulation have confirmed the efficiency of the adaptive quantization algorithm.

On visual masking estimation for adaptive quantization using steerable filters

A fast and accurate assessment of visual masking effects is desirable while encoding in order to utilize such effects to improve the quality of compressed videos through an adaptive quantization (AQ) scheme. Here, we propose a method of estimating the contrast masking threshold on natural scene patches, using texture cues imparted by steerable filter responses. We then employ the estimated thresholds to perform AQ for AV1 encoding. Our experimental results establish that the proposed method is able to outperform existing visual masking models in terms of estimation performance while being relatively computationally inexpensive than these models, and is also able to improve the variance based AQ algorithm that is currently deployed in the SVT-AV1 codec. Using the multi-scale structural similarity index measure (MS-SSIM) as the quality model, our approach achieves an average BD-rate of -1.82% using the uniform quantization scheme as anchor as compared to 5.83% obtained with the variance based method. We note that the proposed approach produces less visible compression artifacts than the variance based AQ approach at lower bitrates, while maintaining similar encoding complexity.

An adaptive spatio-temporal perception aware quantization algorithm for AVS2

Adaptive quantization proves to be an effective tool to improve coding performance. In this paper, we propose an adaptive spatiotemporal perception aware quantization algorithm to increase subjective coding performance. To measure the spatiotemporally perceptual redundancy, the perceptual complexity models are firstly established with spatial and temporal characteristics respectively. With the help of the models, the adaptive spatial and temporal quantization parameter (QP) offsets are then calculated for each coding tree unit (CTU), respectively. Finally, the perceptually optimal Lagrange multiplier of each CTU is determined with the spatial–temporal QP offset. Experimental results show that the proposed algorithm reduces 8.6% and 8.4% Bjontegaard-Delta Rate (BD-Rate) with Structural Similarity Index Metric (SSIM) in average over the second generation of Audio Video Coding Standard (AVS2) reference software RD17.0 in Low-Delay-P (LDP) and Random-Access (RA) configurations, respectively. The subjective assessment proves that the proposed algorithm can reduce the bitrates with the same subjective quality significantly.

Efficient physical layer key generation technique in wireless communications

Wireless communications between two devices can be protected by secret keys. However, existing key generation schemes suffer from the high bit disagreement rate and low bit generation rate. In this paper, we propose an efficient physical layer key generation scheme by exploring the Received Signal Strength (RSS) of signals. In order to reduce the high mismatch rate of the measurements and to increase the key generation rate, a pair of transmitter and receiver separately apply adaptive quantization algorithm for quantifying the measurements. Then, we implement a randomness extractor to further increase key generation rate and ensure randomness of generated of keys. Several real-world experiments are implemented to verify the effectiveness of the proposed scheme. The results show that compared with the other related schemes, our scheme performs better in bit generation rate, bit disagreement rate, and randomness.

Design and Implementation of Raw Data Compression System for Subsurface Detection SAR Based on FPGA

Synthetic aperture radar (SAR) has a wide range of applications in the field of remote sensing. Subject to the bandwidth and resource limitations of the onboard system, it is necessary to compress the raw data obtained from SAR. The traditional algorithms for the compression of SAR raw data result in loss of weak signals such as the essential subsurface signal in subsurface detection, leading to problems in practical applications. In order to solve the aforementioned problem, the present study proposes a novel SAR data compression algorithm (preserve weak signal based on block adaptive quantization) that aims to preserve the subsurface signals on the basis of the traditional block adaptive quantization algorithm and provides its corresponding field-programmable gate array implementation. The implementation of the system holds a certain application value with great advantages in the optimized resources and speed, the optional automatic working mode, and the strong scalability.

An Improved BAQ Encoding and Decoding Method for Improving the Quantized SNR of SAR Raw Data.

When the original echo data of SAR are saturated for quantization, the performance of the commonly used block adaptive quantization (BAQ) algorithm will be degraded, which will degrade the imaging quality. This article proposes an improved Llody-Max codec method, which only needs to change the codec look-up table to get better quantization performance when the original echo is saturated. The simulation results show that the proposed method can reduce the quantization power loss, improve the echo signal-to-noise ratio (SNR), and reduce the influence of quantization saturation on the scattering mechanism of polarized SAR data, which have good practical application value.

Model compression of SDM‐based face alignment for mobile applications

Face alignment could be widely used in face recognition, expression recognition, face-based AR applications etc. Cascaded-regression-based face alignment algorithms have been popular in recent years for their low computational costs and impressive results in uncontrolled scenarios. Unfortunately, the size of the trained model is quite large for cascaded-regression-based methods which makes it unsuitable for commercial applications on mobile phones. In this study, the authors proposed a data compression method for the trained model of the supervised descent method (SDM). Firstly, the distribution of the model data was estimated using a non-parametric method. Then an adaptive quantisation algorithm was proposed to quantise the model data. Finally, their adaptive quantisation algorithm was tightly coupled with the SDM training process to fine tune the results. The quantitative experimental results proved that their proposed method could compress the data model to <20% of its original size without hurting the performances. The proposed method has been integrated into a mobile AR application, subjective evaluations proved that the proposed compression method could provide similar visual effects compared with the uncompressed counterpart.

Bilateral adaptive quantization in HEVC

In this paper, we proposed an adaptive quantization algorithm for High Efficiency Video Coding (HEVC) to boost the encoding performance. The transform coefficients in a Transform Unit (TU) inherit the energy concentration property. However, they are equally quantized before entropy coding. With equal quantization technique, the energy distribution of transform coefficients and the scanning pattern following the quantization stage is not properly considered. In order to quantize the coefficients adaptively, we proposed an improved algorithm to quantize the coefficients. For each coefficient, both the magnitude and its ordinal number scanned in entropy coding process are taken into account. The quantization parameter of each coefficient in a TU is adaptively calculated by the bilateral factors accordingly. We tested our method on the latest HM16.0. An average performance of -0.27% on BD-Rate and 7.07% computing time saving are achieved in the case of the commonly used Low Delay P configuration, which demonstrated the effectiveness of the proposed algorithm.

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom Gated Recurrent Unit-based detector and developed a method for the detector parameters selection.Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the dataset intended for real-life experiments and model training was composed of the signals acquired from a new type of magnet, to be used during High-Luminosity Large Hadron Collider project. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art One Class Support Vector Machine (OC-SVM) reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties.It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the setup with the lowest maximum false anomaly length of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.

Saturation-aware human attention region of interest algorithm for efficient video compression

We propose a saturation-aware human attention region-of-interest (SA-HAROI) video compression method that performs a perceptual adaptive quantization algorithm on video frames as a function of the distribution of their luminance, motion vector, and color saturation. Our work is an application of a psycho-visual study that demonstrated that human attention automatically enhanced perceived saturation. Consequently, the adaptive quantization phase of our compression algorithm is characterized by a luminance and saturation-aware just noticeable distortion (JND) function. After running multiple experiments on 18 videos with various resolutions ranging from QCIF to 4 K, results showed that our method achieves higher compression than that of both the H.264/AVC JM and the HEVC HM while maintaining subjective quality. We observed that in comparison to both implementation of the standards (JM and HM), for an IPPP coding structure, the performance of our algorithm culminated with HD and 4 K videos yielding a bit rate reduction averaging 15% and an encoding time reduction of about 20% in certain cases. Finally, after comparing our method to other similar techniques, we concluded that saturation is a significant parameter in the improvement of video compression.

The Security Network Coding System With Physical Layer Key Generation in Two-Way Relay Networks

Network coding is one of the key technologies to improve the throughput, efficiency, and reliability of two-way relay networks (TWRNs). However, it also brings some new security problems when the physical layer security is considered. In this paper, we present security network coding schemes with key generation from multipath channels to enhance the security performance for TWRN. First, a joint key generation (JKG) approach is proposed, which generates secret keys based on channel impulse responses of multipath channels. Different from the traditional approaches, key exchange is not necessary for the JKG approach such that the eavesdroppers cannot obtain any information about secret keys. Then, an adaptive quantization algorithm is proposed to adaptively choose different parameters for quantization in key generation, which can improve the secret key rate with an acceptable secret key disagreement probability and can still work even if the SNR is low. Finally, security network coding systems are constructed to combine the key generation approaches with the proposed algorithm. Simulation results show that the proposed schemes are valid and secure against wiretap attacks.

A perceptually temporal adaptive quantization algorithm for HEVC

Adaptive quantization (AQ) proves to be an effective coding tool to improve the performance of video coding. This paper presents a perceptually temporal AQ method to improve the subjective coding performance for High Efficiency Video Coding (HEVC). We first put forward a perceptual quality oriented motion estimation algorithm, which is conducted with a spatial-temporal just noticeable distortion (JND) model. Then one perceptual feature in temporal domain is proposed to develop our AQ method, which can generate different quantization parameter (QP) offsets for each coding unit (CU). The proposed method fully utilizes the temporal and perceptual characteristics of each CU, which can produce more visual-friendly QP offsets distribution. Experiments are conducted on HM16.0 (HEVC reference software), and with SSIM (Structure Similarity Index Metric) as the distortion metric, more than 8.08% and 7.95% rate savings can be obtained for Low-Delay-P (LDP) and Low-Delay-B (LDB) configurations on average, respectively. The subjective quality evaluation demonstrates that the proposed AQ method can achieve comparable visual quality as the HM16.0 while the proposed method can yield remarkable bitrate reductions.

Adaptive Vector Quantization for Lossy Compression of Image Sequences

In this work, we present a scheme for the lossy compression of image sequences, based on the Adaptive Vector Quantization (AVQ) algorithm. The AVQ algorithm is a lossy compression algorithm for grayscale images, which processes the input data in a single-pass, by using the properties of the vector quantization to approximate data. First, we review the key aspects of the AVQ algorithm and, subsequently, we outline the basic concepts and the design choices behind the proposed scheme. Finally, we report the experimental results, which highlight an improvement in compression performances when our scheme is compared with the AVQ algorithm.

An adaptive hybrid pattern for noise-robust texture analysis

Local binary patterns (LBP) achieve great success in texture analysis, however they are not robust to noise. The two reasons for such disadvantage of LBP schemes are (1) they encode the texture spatial structure based only on local information which is sensitive to noise and (2) they use exact values as the quantization thresholds, which make the extracted features sensitive to small changes in the input image. In this paper, we propose a noise-robust adaptive hybrid pattern (AHP) for noised texture analysis. In our scheme, two solutions from the perspective of texture description model and quantization algorithm have been developed to reduce the feature׳s noise sensitiveness. First, a hybrid texture description model is proposed. In this model, the global texture spatial structure which is depicted by a global description model is encoded with the primitive microfeature for texture description. Second, we develop an adaptive quantization algorithm in which equal probability quantization is utilized to achieve the maximum partition entropy. Higher noise-tolerance can be obtained with the minimum lost information in the quantization process. The experimental results of texture classification on two texture databases with three different types of noise show that our approach leads significant improvement in noised texture analysis. Furthermore, our scheme achieves state-of-the-art performance in noisy face recognition.

Robust Adaptive Video Watermarking Algorithm based on Dual Transform Domains

In this paper, we propose a robust video watermarking algorithm based on dual transform domain using the adaptive quantization method. In order to increase the robustness of watermark, Particle Swarm Optimization algorithm is used for embedding watermarks. Firstly, one-level wavelet decomposition is employed for the two-dimensional carrier signal by using the characteristics of the wavelet multi-resolution analysis, and then the lowfrequency and middle-frequency coefficients are extracted. After dividing the coefficients into small blocks, discrete cosine transform is applied to each block coefficient. Then, the adaptive quantization algorithm is designed. The optimal embedding positions in the two dimensional space of the carrier signal are determined dynamically by using Particle Swarm Optimization algorithm, and the coefficients in the optimal embedding positions are also extracted. According to the characteristics of these coefficients, the optimal quantization step sizes are determined dynamically in three dimensional space. Finally, the watermark is embedded into watermarking vector adaptively by using the optimal embedding positions and optimal quantization step sizes, then the watermarked vector will be received. The experimental results show that compared with traditional watermark algorithms, the presented robust watermark algorithm not only eliminate the block effects that might arise in the transform process, but also make full use of Particle Swarm Optimization algorithm to determine the optimal embedding positions and optimal quantization step sizes of the watermark embedding algorithm. It has implemented the adaptive embedding and increased the non-sentience and robustness of the watermark. A series of attacks and comparative experiments show that the performance of the proposed algorithm is better than traditional algorithms.

Block Adaptive Scalar-Vector Quantization Used to Compress SAR Raw Data

A block adaptive scalar-vector quantization (BASVQ) algorithm is put forward in this paper, namely, scalar quantization is applied when data blocks satisfy Gaussian distribution while vector quantization is applied when doesnt satisfy. The experiments demonstrate that the performance of BASVQ algorithm surpasses that of BAQ algorithm. The BASVQ algorithm has practical value in some degree.

A universal adaptive vector quantization algorithm for space-borne SAR raw data

Codebook of conventional VQ cannot be generally used and needs real time onboard updating, which is hard to implement in spaceborne SAR system. In order to solve this problem, this paper analyses the characteristic of space-borne SAR raw data firstly, and then utilizes the distortion function of multidimensional space as criterion, and finally the adaptive code book design algorithm is proposed according to the joint probability density function of the input data. Besides, the feasibility of the new algorithm in cascade with entropy coding and the robustness of the algorithm when error occurs during transmission are analysed based on the encoding and decoding scheme. Experimental results of real data show that codebook deriving from the new algorithm can be generally used and designed off-line, which makes VQ a practical algorithm for space-borne SAR raw data compression.