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predictive quantization of Range-Focused SAR Raw Data

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2012年第4期50卷 1340-1348页

作者： Ikuma, Takeshi Naraghi-Pour, Mort Lewis, Thomas Louisiana State Univ Dept Otolaryngol Heath Sci Ctr New Orleans LA 70112 USA Louisiana State Univ Dept Elect & Comp Engn Baton Rouge LA 70803 USA USAF Res Lab RWGS Eglin AFB FL 32542 USA

Synthetic aperture radar (SAR) systems create massive amounts of data which require huge resources for transmission or storage. The limited capacity of the downlink channel demands efficient onboard compression of SAR data. However, SAR raw data exhibit very little correlation which can be exploited in a compression algorithm. Range focusing is shown to increase the data correlation by exposing some of the distinctive features of the scene under surveillance. In this paper, we first present analysis of spotlight-mode SAR to show the source of the increased correlation in the range-focused data. Next, we propose two algorithms-transform-domain block predictive quantization (TD-BPQ) and transform-domain block predictive trellis-coded quantization (TD-BPTCQ)-for the compression of the range-focused data. Experimental results indicate that, at the rate of 1 bit/sample, and for similar or lower computational complexity, TD-BPQ and TD-BPTCQ outperform the best method proposed in the literature by 1.5 and 2.3 dB in signal-to-quantization-noise ratio, respectively. Similar improvements are observed for the rate of 2 bits/sample.

关键词： Autoregressive (AR) model differential pulse code modulation (DPCM) predictive quantization spotlight-mode SAR synthetic aperture radar (SAR) trellis-coded quantization (TCQ)

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predictive quantization OF DECHIRPED SPOTLIGHT-MODE SAR RAW DATA IN TRANSFORM DOMAIN 30

PREDICTIVE QUANTIZATION OF DECHIRPED SPOTLIGHT-MODE SAR RAW ...

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30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action

作者： Ikuma, Takeshi Naraghi-Pour, Mort Lewis, Thomas Louisiana State Univ Dept Elect & Comp Engn Baton Rouge LA 70803 USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9781424495665

Synthetic aperture radar (SAR) systems collect large volumes of data that must be transmitted to a ground station for storage and processing. However, given the limited bandwidth of the downlink channel it is imperative that SAR data be compressed before transmission. While it is commonly believed that raw SAR data is uncorrelated, it is shown in [1] that the inverse Fourier transform of spotlight-mode SAR exhibits non-negligible correlation that can be exploited in a predictive quantization scheme. In this paper, we propose two predictive quantization algorithms-transform-domain block predictive quantization (TD-BPQ), and transform-domain block predictive vector quantization (TD-BPVQ)-to encode dechirp-on-receive spotlight-mode SAR raw data. Experimental results indicate that, on average, TD-BPQ and TD-BPVQ outperform the well known block adaptive quantization (BAQ) by 5 and 6 dB, respectively.

关键词： SAR data compression predictive quantization DPCM linear prediction autoregressive analysis

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predictive Vector quantization for Multicell Cooperation with Delayed Limited Feedback

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2013年第6期12卷 2588-2597页

作者： Bhagavatula, Ramya Heath, Robert W., Jr. ASSIA Inc Redwood City CA 94065 USA Univ Texas Austin Dept Elect & Comp Eng Austin TX 78712 USA

Base station cooperation can use knowledge of the users' channel state information (CSI) at the transmitters to manage co-channel interference. A reasonable way to provide CSI to the base stations is through a finite rate limited feedback channel. Existing multicell limited feedback techniques require a large amount of feedback, which incurs an overhead penalty on the uplink. In this paper, a new feedback approach based on predictive vector quantization (PVQ) is proposed to reduce feedback requirements in multicell systems and provide high resolution CSI at base stations by exploiting temporal correlation in the channels. Transmitter and receiver structures are proposed to implement predictive limited feedback accounting for delay, for signals on the Grassmann manifold. Simulations show that the proposed PVQ framework yields higher sum-rates than memoryless quantization approaches for multicell limited feedback, in a cooperative system using intercell interference nulling.

关键词： Limited feedback multicell coordination predictive quantization MIMO Base stations cellular networks channel state information

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Compressive-sensing recovery of images by context extraction from random samples

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第9期83卷 26711-26732页

作者： Li, Ran Dai, Juan Yang, Yihao Ni, Yulong Sun, Fengyuan Xinyang Normal Univ Sch Comp & Informat Technol Nanhu Rd 237 Xinyang 464000 Henan Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Wireless Wideband Commun & Signal Jinji Rd 1 Guilin 541004 Guangxi Peoples R China

Image Compressive Sensing (CS) provides a scheme of low-complex image coding, but coping with the recovery quality has been a challenge. Even the excessive investment of computations into recovery cannot prevent the quality degradation due to the lack of appropriate allocation for sampling resources. In light of this, this paper fuses a context-based allocation into image CS in order to improve the recovery quality with fewer computations. Independent of original pixels, the context features of blocks are extracted from random CS samples. According to the block-based distribution on context features, more CS samples are allocated to non-sparse regions and fewer to sparse regions. The proposed context-based allocation enables a linear recovery model to accurately recover images. The contributions of this paper include: (1) an adaptive allocation involving the context features extracted from CS samples, (2) a padding Differential Pulse Code Modulation (DPCM) to quantize the adaptive CS samples, and (3) a regrouping module to improve the quality of linear recovery. Experimental results show the proposed image CS system objectively and subjectively improves the recovery quality of an image while guaranteeing a low computational complexity, e.g., it achieves average 30.85 dB PSNR value on the five 512x512\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$512 \times 512$$\end{document} test images, and costs about 10 seconds on a computer with 3.30 GHz CPU and 8 GB RAM. Besides, the proposed system presents a competitive performance to the recent deep-learned image CS systems.

关键词： Compressive Sensing (CS) Image coding Self-similarity descriptor Context extraction predictive quantization Linear recovery

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Multichannel ΣΔ ADCs With Integrated Feedback Beamformers to Cancel Interfering Communication Signals

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第5期59卷 2211-2222页

作者： Venkateswaran, Vijay van der Veen, Alle-Jan Delft Univ Technol Fac EEMCS NL-2628 CD Delft Netherlands

In multiuser multiantenna communication receivers, the use of high resolution ADCs is costly. In the presence of (strong) interference, more bits are used than would be necessary for quantizing only the signal of interest. Thus, if it is possible to cancel the interference in the analog domain, considerable savings can be realized. In this paper, we exploit the fact that a multiantenna receiver consists of a bank of ADCs and propose a new architecture, wherein a feedback beamformer (FBB) takes a linear combination of the ADC outputs and feeds back the result to be subtracted at the input. This ADC architecture is especially compatible with existing Sigma Delta ADCs, that already consist of a digital-to-analog converter (DAC) in the feedback loop and enables sophisticated source separation algorithms designed in the digital baseband to cancel the interfering users in the analog domain. Subsequently, the Sigma Delta ADCs digitize only the desired user signals and achieve considerable savings in power consumption. Using a mean squared error criterion and assuming that a training sequence is available, we present an algorithm to design the weights of the FBB. The interference suppression and power savings of the proposed approach are demonstrated via simulation results.

关键词： Linear prediction multiantenna/MIMO systems oversampling Sigma Delta ADCs predictive quantization source separation

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predictive Coding of Bit Loading for Time-Correlated MIMO Channels With a Decision Feedback Receiver

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第13期63卷 3376-3386页

作者： Li, Chien-Chang Lin, Yuan-Pei MediaTek Inc Hsinchu 300 Taiwan Natl Chiao Tung Univ Dept Elect Engn Hsinchu 300 Taiwan

In this paper, we consider variable-rate transmission over a slowly varying multiple-input multiple-output (MIMO) channel with a decision feedback receiver. The transmission rate is adapted to the channel by dynamically assigning bits to the subchannels of the MIMO system. predictive quantization is used for the feedback of bit loading to take advantage of the time correlation inherited from the temporally correlated channel. Due to the use of decision feedback at the receiver, the bit loading is related to the Cholesky decomposition of the channel Gram matrix. Assuming the channel is modeled by a slowly varying Gauss-Markov process, we show that the nested submatrices generated during the process of Cholesky decomposition can be updated as time evolves. Based on the update, we derive the optimal predictor of the next bit loading for predictive quantization. Furthermore, we derive the statistics of the prediction error, which are then exploited to design the quantizer to achieve a smaller quantization error. Simulations are given to demonstrate that the proposed predictive quantization gives a good approximation of the desired transmission rate with a low feedback rate.

关键词： MIMO limited feedback differential feedback predictive quantization bit loading variable rate transmission

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Hybrid digital-analog coding with bandwidth expansion for correlated Gaussian sources under Rayleigh fading

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2017年第1期2017卷 1页

作者： Yahampath, Pradeepa Univ Manitoba Dept Elect & Comp Engn Winnipeg MB R3T 5V6 Canada

Consider communicating a correlated Gaussian source over a Rayleigh fading channel with no knowledge of the channel signal-to-noise ratio (CSNR) at the transmitter. In this case, a digital system cannot be optimal for a range of CSNRs. Analog transmission however is optimal at all CSNRs, if the source and channel are memoryless and bandwidth matched. This paper presents new hybrid digital-analog (HDA) systems for sources with memory and channels with bandwidth expansion, which outperform both digital-only and analog-only systems over a wide range of CSNRs. The digital part is either a predictive quantizer or a transform code, used to achieve a coding gain. Analog part uses linear encoding to transmit the quantization error which improves the performance under CSNR variations. The hybrid encoder is optimized to achieve the minimum AMMSE (average minimum mean square error) over the CSNR distribution. To this end, analytical expressions are derived for the AMMSE of asymptotically optimal systems. It is shown that the outage CSNR of the channel code and the analog-digital power allocation must be jointly optimized to achieve the minimum AMMSE. In the case of HDA predictive quantization, a simple algorithm is presented to solve the optimization problem. Experimental results are presented for both Gauss-Markov sources and speech signals.

关键词： Hybrid digital-analog coding predictive quantization Transform coding Fading channels Speech coding

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Incremental maintenance of online summaries over multiple streams

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2008年第2期20卷 216-229页

作者： Altiparmak, Fatih Tuncel, Ertem Ferhatosmanoglu, Hakan Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Univ Calif Riverside Dept Elect Engn Riverside CA 92521 USA

We propose a novel approach based on predictive quantization (PQ) for online summarization of multiple time-varying data streams. A synopsis over a sliding window of most recent entries is computed in one pass and dynamically updated in constant time. The correlation between consecutive data elements is effectively taken into account without the need for preprocessing. We extend PQ to multiple streams and propose structures for real-time summarization and querying of a massive number of streams. Queries on any subsequence of a sliding window over multiple streams are processed in real time. We examine each component of the proposed approach, prediction, and quantization separately and investigate the space-accuracy trade-off for synopsis generation. Complementing the theoretical optimality of PQ-based approaches, we show that the proposed technique, even for very short prediction windows, significantly outperforms the current techniques for a wide variety of query types on both synthetic and real data sets.

关键词： multiple streams predictive quantization data summarization synopsis online update time-varying data

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Reconstruction of predictively encoded signals over noisy channels using a sequence MMSE decoder

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IEEE TRANSACTIONS ON COMMUNICATIONS 2004年第8期52卷 1292-1301页

作者： Lahouti, F Khandani, AK Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

In this paper, we consider the problem of decoding a predictively encoded signal over a noisy channel when there is a residual redundancy (captured by a gamma-order Markov model) in the sequence of transmitted data. Our objective is to minimize the mean-squared error (MSE) in the reconstruction of the original signal (input to the predictive source coder). The problem is formulated and solved through minimum mean-squared error (MMSE) decoding of a sequence of samples over a memoryless noisy channel. The related previous works include several maximum a posteriori (MAP) and MMSE-based decoders. The MAP-based approaches are suboptimal when the performance criterion is the MSE. On the other hand, the previously known MMSE-based approaches are suboptimal, since they are designed to efficiently reconstruct the data samples received (the prediction residues) rather than the original signal. The proposed scheme is set up by modeling the source-coder-produced symbols and their redundancy with a trellis structure. Methods are presented to optimize the solutions in terms of complexity. Numerical results and comparisons are provided, which demonstrate the effectiveness of the proposed techniques.

关键词： differential pulse code modulation (DPCM) forward-backward recursion joint source-channel coding Markov sources maximum a posteriori (MAP) detection minimum mean-squared error (MMSE) estimation predictive quantization residual redundancies source decoding

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IMPROVING SCALAR quantization FOR CORRELATED PROCESSES USING ADAPTIVE CODEBOOKS ONLY AT THE RECEIVER 22

IMPROVING SCALAR QUANTIZATION FOR CORRELATED PROCESSES USING...

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22nd European Signal Processing Conference (EUSIPCO)

作者： Han, Sai Fingscheidt, Tim Tech Univ Carolo Wilhelmina Braunschweig Inst Commun Technol Schleinitzstr 22 D-38106 Braunschweig Germany

ISBN: (纸本)9780992862619

Lloyd-Max quantization (LMQ) is a widely used scalar non-uniform quantization approach targeting for the minimum mean squared error (MMSE). Once designed, the quantizer codebook is fixed over time and does not take advantage of possible correlations in the input signals. Exploiting correlation in scalar quantization could be achieved by predictive quantization, however, for the price of a higher bit error sensitivity. In order to improve the Lloyd-Max quantizer performance for correlated processes without encoder-sided prediction, a novel scalar decoding approach utilizing the correlation of input signals is proposed in this paper. Based on previously received samples, the current sample can be predicted a priori. Thereafter, a quantization codebook adapted over time will be generated according to the prediction error probability density function. Compared to the standard LMQ, distinct improvement is achieved with our receiver in error-free and error-prone transmission conditions, both with hard-decision and soft-decision decoding.

关键词： Lloyd-Max quantization correlated process predictive quantization probability density function soft-decision decoding

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