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Relevant bibliographies by topics / FIRST QUANTIZATION ESTIMATION

Academic literature on the topic 'FIRST QUANTIZATION ESTIMATION'

Author: Grafiati

Published: 11 March 2023

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Journal articles on the topic "FIRST QUANTIZATION ESTIMATION"

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Gao, Chao, Guorong Zhao, Jianhua Lu, and Shuang Pan. "Decentralized state estimation for networked spatial-navigation systems with mixed time-delays and quantized complementary measurements: The moving horizon case." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 11 (June 8, 2017): 2160–77. http://dx.doi.org/10.1177/0954410017712277.

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In this paper, the navigational state estimation problem is investigated for a class of networked spatial-navigation systems with quantization effects, mixed time-delays, and network-based observations (i.e. complementary measurements and regional estimations). A decentralized moving horizon estimation approach, featuring complementary reorganization and recursive procedure, is proposed to tackle this problem. First, through the proposed reorganized scheme, a random delayed system with complementary observations is reconstructed into an equivalent delay-free one without dimensional augment. Second, with this equivalent system, a robust moving horizon estimation scheme is presented as a uniform estimator for the navigational states. Third, for the demand of real-time estimate, the recursive form of decentralized moving horizon estimation approach is developed. Furthermore, a collective estimation is obtained through the weighted fusion of two parts, i.e. complementary measurements based estimation, and regional estimations directly from the neighbors. The convergence properties of the proposed estimator are also studied. The obtained stability condition implicitly establishes a relation between the upper bound of the estimation error and two parameters, i.e. quantization density and delay occur probability. Finally, an application example to networked unmanned aerial vehicles is presented and comparative simulations demonstrate the main features of the proposed method.

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Galvan, Fausto, Giovanni Puglisi, Arcangelo Ranieri Bruna, and Sebastiano Battiato. "First Quantization Matrix Estimation From Double Compressed JPEG Images." IEEE Transactions on Information Forensics and Security 9, no. 8 (August 2014): 1299–310. http://dx.doi.org/10.1109/tifs.2014.2330312.

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Sun, Baoyan, Jun Hu, and Yan Gao. "Variance-constrained robust $ H_{\infty} $ state estimation for discrete time-varying uncertain neural networks with uniform quantization." AIMS Mathematics 7, no. 8 (2022): 14227–48. http://dx.doi.org/10.3934/math.2022784.

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<abstract><p>In this paper, we consider the robust $ H_{\infty} $ state estimation (SE) problem for a class of discrete time-varying uncertain neural networks (DTVUNNs) with uniform quantization and time-delay under variance constraints. In order to reflect the actual situation for the dynamic system, the constant time-delay is considered. In addition, the measurement output is first quantized by a uniform quantizer and then transmitted through a communication channel. The main purpose is to design a time-varying finite-horizon state estimator such that, for both the uniform quantization and time-delay, some sufficient criteria are obtained for the estimation error (EE) system to satisfy the error variance boundedness and the $ H_{\infty} $ performance constraint. With the help of stochastic analysis technique, a new $ H_{\infty} $ SE algorithm without resorting the augmentation method is proposed for DTVUNNs with uniform quantization. Finally, a simulation example is given to illustrate the feasibility and validity of the proposed variance-constrained robust $ H_{\infty} $ SE method.</p></abstract>

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Liu, Guiyun, Jing Yao, Yonggui Liu, Hongbin Chen, and Dong Tang. "Channel-Aware Adaptive Quantization Method for Source Localization in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/214081.

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This paper considers the problem of source localization using quantized observations in wireless sensor networks where, due to bandwidth constraint, each sensor’s observation is usually quantized into one bit of information. First, a channel-aware adaptive quantization scheme for target location estimation is proposed and local sensor nodes dynamically adjust their quantization thresholds according to the position-based information sequence. The novelty of the proposed approach comes from the fact that the scheme not only adopts the distributed adaptive quantization instead of the conventional fixed quantization, but also incorporates the statistics of imperfect wireless channels between sensors and the fusion center (binary symmetric channels). Furthermore, the appropriate maximum likelihood estimator (MLE), the performance metric Cramér-Rao lower bound (CRLB), and a sufficient condition for the Fisher information matrix being positive definite are derived, respectively. Simulation results are presented to show that the appropriated CRLB is less than the fixed quantization channel-aware CRLB and the proposed MLE will approach their CRLB when the number of sensors is large enough.

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Tadic, Predrag, Zeljko Djurovic, and Branko Kovacevic. "Analysis of speech waveform quantization methods." Journal of Automatic Control 18, no. 1 (2008): 19–22. http://dx.doi.org/10.2298/jac0801019t.

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Digitalization, consisting of sampling and quantization, is the first step in any digital signal processing algorithm. In most cases, the quantization is uniform. However, having knowledge of certain stochastic attributes of the signal (namely, the probability density function, or pdf), quantization can be made more efficient, in the sense of achieving a greater signal to quantization noise ratio. This means that narrower channel bandwidths are required for transmitting a signal of the same quality. Alternatively, if signal storage is of interest, rather than transmission, considerable savings in memory space can be made. This paper presents several available methods for speech signal pdf estimation, and quantizer optimization in the sense of minimizing the quantization error power.

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Battiato, Sebastiano, Oliver Giudice, Francesco Guarnera, and Giovanni Puglisi. "First Quantization Estimation by a Robust Data Exploitation Strategy of DCT Coefficients." IEEE Access 9 (2021): 73110–20. http://dx.doi.org/10.1109/access.2021.3080576.

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Yao, Heng, Hongbin Wei, Chuan Qin, and Xinpeng Zhang. "An improved first quantization matrix estimation for nonaligned double compressed JPEG images." Signal Processing 170 (May 2020): 107430. http://dx.doi.org/10.1016/j.sigpro.2019.107430.

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Xue, Fei, Ziyi Ye, Wei Lu, Hongmei Liu, and Bin Li. "MSE period based estimation of first quantization step in double compressed JPEG images." Signal Processing: Image Communication 57 (September 2017): 76–83. http://dx.doi.org/10.1016/j.image.2017.05.008.

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HoangVan, Xiem. "Adaptive Quantization Parameter Estimation for HEVC Based Surveillance Scalable Video Coding." Electronics 9, no. 6 (May 30, 2020): 915. http://dx.doi.org/10.3390/electronics9060915.

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Visual surveillance systems have been playing a vital role in human modern life with a large number of applications, ranging from remote home management, public security to traffic monitoring. The recent High Efficiency Video Coding (HEVC) scalable extension, namely SHVC, provides not only the compression efficiency but also the adaptive streaming capability. However, SHVC is originally designed for videos captured from generic scenes rather than from visual surveillance systems. In this paper, we propose a novel HEVC based surveillance scalable video coding (SSVC) framework. First, to achieve high quality inter prediction, we propose a long-term reference coding method, which adaptively exploits the temporal correlation among frames in surveillance video. Second, to optimize the SSVC compression performance, we design a quantization parameter adaptation mechanism in which the relationship between SSVC rate-distortion (RD) performance and the quantization parameter is statistically modeled by a fourth-order polynomial function. Afterwards, an appropriate quantization parameter is derived for frames at long-term reference position. Experiments conducted for a common set of surveillance videos have shown that the proposed SSVC significantly outperforms the relevant SHVC standard, notably by around 6.9% and 12.6% bitrate saving for the low delay (LD) and random access (RA) coding configurations, respectively while still providing a similar perceptual decoded frame quality.

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Peric, Zoran, Milan Tancic, Nikola Simic, and Vladimir Despotovic. "Simple Speech Transform Coding Scheme using Forward Adaptive Quantization for Discrete Input Signal." Information Technology And Control 48, no. 3 (September 24, 2019): 454–63. http://dx.doi.org/10.5755/j01.itc.48.3.21685.

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We propose a speech coding scheme based on the simple transform coding and forward adaptive quantization for discrete input signal processing in this paper. The quasi-logarithmic quantizer is applied to discretization of continuous input signal, i.e. for preparing discrete input. The application of forward adaptation based on the input signal variance provides more efficient bandwidth usage, whereas utilization of transform coding provides sub-sequences with more predictable signal characteristics that ensure higher quality of signal reconstruction at the receiving end. In order to provide additional compression, transform coding precedes adaptive quantization. As an objective measure of system performance we use signal-to-quantization-noise ratio. Sysem performance is discussed for two typical cases. In the first case, we consider that the information about continuous signal variance is available whereas the second case considers system performance estimation when we know only the information about discretized signal variance which means that there is a loss of input signal information. The main goal of such performance estimation comparison of the proposed speech signal coding model is to explore what is the objectivity of performance if we do not have information about a continuous source, which is a common phenomenon in digital systems.

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Book chapters on the topic "FIRST QUANTIZATION ESTIMATION"

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Battiato, Sebastiano, Oliver Giudice, Francesco Guarnera, and Giovanni Puglisi. "In-Depth DCT Coefficient Distribution Analysis for First Quantization Estimation." In Pattern Recognition. ICPR International Workshops and Challenges, 573–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68780-9_45.

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Conference papers on the topic "FIRST QUANTIZATION ESTIMATION"

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Puglisi, Giovanni, Arcangelo Ranieri Bruna, Fausto Galvan, and Sebastiano Battiato. "First JPEG quantization matrix estimation based on histogram analysis." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738927.

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Fan, Wei, Kai Wang, François Cayre, and Zhang Xiong. "JPEG anti-forensics using non-parametric DCT quantization noise estimation and natural image statistics." In the first ACM workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2482513.2482536.

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Battiato, Sebastiano, Oliver Giudice, Francesco Guarnera, and Giovanni Puglisi. "Computational Data Analysis for First Quantization Estimation on JPEG Double Compressed Images." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412528.

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Dalmia, Nandita, and Manish Okade. "First quantization matrix estimation for double compressed JPEG images utilizing novel DCT histogram selection strategy." In the Tenth Indian Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/3009977.3010067.

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Gavriely, A., S. Shitz, and Y. Y. Zeevi. "Image sampling based on sine-wave and zero crossings." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.thx3.

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Provided certain conditions are satisfied, the zero crossings of a signal constitute a sampling set. Indeed we have shown that images can be reconstructed from such a set. Considering this or other sampling theorems, two fundamental issues must be addressed. First, the existence of a broad class of signals satisfying the conditions in which recovery is guaranteed, and second, the existence of practical stable reconstruction algorithms. In the case of zero crossing the above conditions are not necessarily satisfied. The sine-wave-crossing (SWC) approach overcomes these difficulties. The theoretical framework, independently established by some researchers, provides several reconstruction algorithms for 1-D signals, stable in the sense that small errors in the sampling process result in relatively small errors in the reconstructed signal. Extending these results to SWC contours, we reconstruct images by applying an interpolation algorithm similar to the one used in recovering a signal sampled uniformly at Nyquist rate. Investigating the effects of crossing-location estimation on signal reconstruction, this work highlights the similarity between effects of quantization along the amplitude axis and spatial axes. Adopting a stochastic approach, we provide bounds on the reconstruction error of bandlimited and almost bandlimited signals. The bounds account for the effects of quantization levels, amplitude, and frequency of the added sine-wave and out-of-band energy, on the resultant m.s.e. Computations indicate that the bounds are tight.

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Amini, Mohammad Reza, Mahdi Shahbakhti, and Selina Pan. "MIMO First and Second Order Discrete Sliding Mode Controls of Uncertain Linear Systems Under Implementation Imprecisions." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5010.

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The performance of a conventional model-based controller significantly depends on the accuracy of the modeled dynamics. The model of a plant’s dynamics is subjected to errors in estimating the numerical values of the physical parameters, and variations over operating environment conditions and time. These errors and variations in the parameters of a model are the major sources of uncertainty within the controller structure. Digital implementation of controller software on an actual electronic control unit (ECU) introduces another layer of uncertainty at the controller inputs/outputs. The implementation uncertainties are mostly due to data sampling and quantization via the analog-to-digital conversion (ADC) unit. The failure to address the model and ADC uncertainties during the early stages of a controller design cycle results in a costly and time consuming verification and validation (V&V) process. In this paper, new formulations of the first and second order discrete sliding mode controllers (DSMC) are presented for a general class of uncertain linear systems. The knowledge of the ADC imprecisions is incorporated into the proposed DSMCs via an online ADC uncertainty prediction mechanism to improve the controller robustness characteristics. Moreover, the DSMCs are equipped with adaptation laws to remove two different types of modeling uncertainties (multiplicative and additive) from the parameters of the linear system model. The proposed adaptive DSMCs are evaluated on a DC motor speed control problem in real-time using a processor-in-the-loop (PIL) setup with an actual ECU. The results show that the proposed SISO and MIMO second order DSMCs improve the conventional SISO first order DSMC tracking performance by 69% and 84%, respectively. Moreover, the proposed adaptation mechanism is able to remove the uncertainties in the model by up to 90%.

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