Data compression by redundancy reduction

Because of the increasingly pressing problem of spectrum overcrowding in data transmission channels, it is becoming more and more necessary to develop schemes to optimize the use of the available frequencies. The Proceedings of the IEEE is devoting its March issue to the subject of ``Redundancy Reduction and Bandwidth Saving.' This article, timed to coincide with the Proceedings issue, briefly describes some of the salient features of the various approaches to redundancy reduction.     

Removing label ambiguity in learning-based visual saliency estimation

Visual saliency is a useful clue to depict visually important image/video contents in many multimedia applications. In visual saliency estimation, a feasible solution is to learn a "feature-saliency" mapping model from the user data obtained by manually labeling activities or eye-tracking devices. However, label ambiguities may also arise due to the inaccurate and inadequate user data. To process the noisy training data, we propose a multi-instance learning to rank approach for visual saliency estimation. In our approach, the correlations between various image patches are incorporated into an ordinal regression framework. By iteratively refining a ranking model and relabeling the image patches with respect to their mutual correlations, the label ambiguities can be effectively removed from the training data. Consequently, visual saliency can be effectively estimated by the ranking model, which can pop out real targets and suppress real distractors. Extensive experiments on two public image data sets show that our approach outperforms 11 state-of-the-art methods remarkably in visual saliency estimation.     

Consecutive reduction of 2-dimensional redundancy

A source-encoding scheme for reducing the 2-dimensional redundancy of facsimile pictures is presented. Each scanning line is decomposed into two subsequences in which a great part of the interline correlation is eliminated. These are then compressed separately with 1-dimensional run-length codes.     

A new deep spatial transformer convolutional neural network for image saliency detection

In this paper we propose a novel deep spatial transformer convolutional neural network (Spatial Net) framework for the detection of salient and abnormal areas in images. The proposed method is general and has three main parts: (1) context information in the image is captured by using convolutional neural networks (CNN) to automatically learn high-level features; (2) to better adapt the CNN model to the saliency task, we redesign the feature sub-network structure to output a 6-dimensional transformation matrix for affine transformation based on the spatial transformer network. Several local features are extracted, which can effectively capture edge pixels in the salient area, meanwhile embedded into the above model to reduce the impact of highlighting background regions; (3) finally, areas of interest are detected by means of the linear combination of global and local feature information. Experimental results demonstrate that Spatial Nets obtain superior detection performance over state-of-the-art algorithms on two popular datasets, requiring less memory and computation to achieve high performance.     

Transmitter redundancy for blind estimation and equalization oftime- and frequency-selective channels

Joint mitigation of time- and frequency-selective fading is an important and challenging problem in mobile communications. Relying on transmitter-induced redundancy, we propose novel channel estimation and symbol recovery approaches for blind identification and equalization of time- and frequency-selective channels, where the time variation is modeled deterministically by a basis expansion. The resulting statistical algorithm enables the usage of a single antenna, dispenses with channel disparity conditions of existing approaches, and allows channel order overestimation. In addition, new deterministic algorithms for generalized OFDM systems are introduced that produce reliable estimates with few data points at high SNR's. Simulations illustrate the approaches developed     

Simple adaptive vectors encoding for two-dimensional redundancy reduction (saver)

An efficient and practical source encoding algorithm for compressing binary facsimile data is presented. The vertical correlation is taken into account by forming vectors and the horizontal correlation is considered by the run-length coding of these vectors. New proposals for an end-of-line codeword and error correction are described.     

Application of redundancy reduction to television bandwidth compression

Redundancy reduction processes have proven highly effective in compressing the bandwidth of pictorial data. Compression is achieved by approximating the video time function with polynomial sequences. This paper presents experimental results obtained by computer simulation, demonstrating the effectiveness of redundancy reduction when applied to different examples of pictorial material. Some of the practical considerations in implementing bandwidth compression systems for both black-and-white and color television are also discussed. Based upon these simulation results and laboratory investigations, it is estimated that video bandwidth reductions from two to four can be accomplished with the present state-of-the-art.     

An integration of bottom-up and top-down salient cues on RGB-D data: saliency from objectness versus non-objectness

Bottom-up and top-down visual cues are two types of information that helps the visual saliency models. These salient cues can be from spatial distributions of the features (space-based saliency) or contextual/task-dependent features (object-based saliency). Saliency models generally incorporate salient cues either in bottom-up or top-down norm separately. In this work, we combine bottom-up and top-down cues from both space- and object-based salient features on RGB-D data. In addition, we also investigated the ability of various pre-trained convolutional neural networks for extracting top-down saliency on color images based on the object dependent feature activation. We demonstrate that combining salient features from color and dept through bottom-up and top-down methods gives significant improvement on the salient object detection with space-based and object-based salient cues. RGB-D saliency integration framework yields promising results compared with the several state-of-the-art-models.     

Residential area extraction based on saliency analysis for high spatial resolution remote sensing images

Traditional residential area extraction methods for remote sensing image depend on classification, segmentation and prior knowledge which are time-consuming and difficult to build. In this paper, an efficient, saliency analysis-based residential area extraction method is proposed. In the proposed model, an adaptive directional prediction-based lifting wavelet transform (ADP-LWT) is introduced to obtain the orientation feature. A logarithm co-occurrence histogram is employed to compute the intensity feature. The color opponency and diagram objection based on the information are proposed to extract color feature from the contrast in the red–green opponent channel. The saliency map is obtained through a weighted combination based on the feature competition and the residential area is extracted by saliency map threshold segmentation. The experimental results reveal that the residential area extracted by our model has more demarcated boundaries and better performance in background subtraction.     

Background estimation and motion saliency detection using total variation-based video decomposition

As human vision system is highly sensitive to motion present in a scene, motion saliency forms an important feature in a video sequence. Motion information is used for video compression, object segmentation, object tracking and in many other applications. Though its applications are extensive, accurate detection of motion in a given video is complex and computationally expensive for the solutions reported in the literature. Decomposing a video into visually similar and residual videos is a robust way to detect motion salient regions. The existing decomposition techniques require large execution time as the standard form of the problem is NP-hard. We propose a novel algorithm which detects the motion salient regions by decomposing the input video into background and residual videos in much lesser time without sacrificing the accuracy of the decomposition. In addition, the proposed algorithm is completely parallelizable that ensures further reduction in computational time with the use of advanced multicore processors.     

结合残留冗余信息与MMSE估计的信源信道联合编解码

文中提出一种利用残留冗余的RDPCM信源信道联合编码系统与最小均方误差估计结合的方法.首先,本文针对联合编码系统修正了SOVA算法,在接收端获得利用残留冗余后的比特似然度;然后利用这些后验信息,对信源预测编码器的输出符号值进行最小均方误差重建后再进行信源译码,从而减小了由于硬判决得到符号值所带来的失真.仿真结果显示这种算法在信噪比的低端最大得到了约2dB的增益.     

宽带循环平稳信号的二维空间谱估计

本文提出了一种新的宽带二维波达方向估计方法。理论分析表明,该方法不需要进行二维搜索,既适用于宽带信号,也适用于窄带信号,且能够实现二维参数的自动配对。通过利用循环平稳特性,该方法能够抑制宽带干扰和加性噪声的影响,从而大大提高了算法的信号检测能力。     

Parameter estimation and error reduction for OFDM-based WLANs

We consider parameter estimation and error reduction for orthogonal frequency-division multiplexing (OFDM) based high-speed wireless local area networks (WLANs). We devise or select algorithms that can provide benefit to the overall system performance and can be efficiently implemented in real-time. In particular, first, we give a channel model which is especially useful for assessing the channel parameter estimation methods devised for OFDM-based WLANs. Second, we provide a sequential method for the estimation of carrier frequency offset (CFO), symbol timing, and channel response by exploiting the structure of the packet preamble specified by the IEEE 802.11a standard. Finally, to correct the residue CFO induced phase error using the pilot tones, we consider maximum-likelihood phase tracking and least-squares phase fitting approaches; to improve the channel estimation accuracy using the decoded data, we present a semiblind channel estimation method; to mitigate the sampling clock induced time delay error, we provide a sampling clock synchronization approach that obviates the need of an automatic frequency control clock recovery circuit. The overall system performance of using our algorithms is demonstrated via several numerical examples.     

Improved spatial smoothing techniques for DOA estimation ofcoherent signals

In the context of coherent signal classification, spatial smoothing is necessary for the application of the eigen-based direction of arrival (DOA) estimation methods. However, the currently known spatial smoothing algorithms not only reduce the effective aperture of the array, but also do not consider the cross correlations of the subarray outputs. An improved spatial smoothing algorithm which can fully utilize the correlations of the array outputs and produce a more stable estimate of the covariance matrix is presented. Simulation results are provided to verify the theoretical prediction. The superiority of this method over the conventional methods is obvious, especially when the SOSR (subarray to overall size ratio) is small     

Image saliency estimation via random walk guided by informativeness and latent signal correlations

Visual saliency is an effective tool for perceptual image processing. In the past decades, many saliency models have been proposed by primarily considering visual cues such as local contrast and global rarity. However, such explicit cues derived only from input stimuli are often insufficient to separate targets from distractors, leading to noisy saliency maps. In fact, the latent cues, especially the latent signal correlations that link visually distinct stimuli (e.g., various parts of a salient target), may also play an important role in saliency estimation. In this paper, we propose a graph-based approach for image saliency estimation by incorporating both explicit visual cues and latent signal correlations. In our approach, the latent correlations between various image patches are first derived according to the statistical prior obtained from 10 million reference images. After that, the informativeness of image patches and their latent correlations are jointly considered to construct a directed graph, on which a random walking process is performed to generate saliency maps that pop-out only the most salient locations. Experimental results show that our approach achieves impressive performances on three public image benchmarks.     

Rank reduction and James-Stein estimation

This correspondence addresses the problem of estimating the signal in a signal-plus-Gaussian-noise model when it is known that the signal lies in a given subspace. An alternative to rank reduction is presented. The new estimator has the remarkable property of having a smaller mean-square error than that of the maximum-likelihood (also least-squares) estimator for all parameter values     

A novel, robust DSP-based indirect rotor position estimation for permanent magnet AC motors without rotor saliency

This paper proposes and implements a novel rotor position sensorless technique for PM AC motor drives, which allows acceleration from standstill and can operate under various practical operating conditions including transient speed changes. The technique developed here relies on the measurement of the phase voltages and currents of the motor. It uses the incremental values of flux linkage, and the back-EMF functions to estimate incremental rotor position. Using a phase-locked loop (PLL) algorithm, an internal closed-loop correction algorithm can correct rotor position estimation drift, which may be due to the motor parameter variations or measurement inaccuracies. The method is implemented in closed-loop using a digital signal processor (DSP), and details of the implementation are provided in the paper. To demonstrate accuracy, robustness and reliability of the position estimation scheme, the paper presents a number of real-time experimental results, including dynamic operating conditions.     

空间谱估计测向系统设计

空间谱估计测向是一种以多元天线阵结合现代数字信号处理为基础的新型测向技术.针对空间谱估计中的典型算法--MUSIC算法,在研究超分辨测向系统的构成、工作原理和硬件实现方案的基础上,提出了一种实现MU-SIC算法的并行处理方案.     

空间谱估计测向系统设计

空间谱估计测向是一种以多元天线阵结合现代数字信号处理为基础的新型测向技术。针对空间谱估计中的典型算法——MUSIC算法,在研究超分辨测向系统的构成、工作原理和硬件实现方案的基础上．提出了一种实现MUSIC算法的并行处理方案。     

Schur type algorithms for spatial LS estimation with highlypipelined architectures

A family of Schur-type spatial least-squares algorithms is presented for solving the spatial LS estimation problem, in which the correlation matrix is neither Toeplitz nor near-Toeplitz, by order recursion. Normalized spatial Levinson- and Schur-type algorithms are also derived. Highly pipelined architectures are designed to realize these recursions. The reflection coefficients are first computed using the spatial Schur type recursions. Then, the forward and backward filter parameters are calculated by the spatial Levinson-type recursions. A pyramid systolic array is demonstrated to calculate not only the filter parameters but also the LDU decomposition of the inverse cross-correlation matrix at every clock phase. This pyramid array can be mapped onto a two-dimensional systolic array which has a simpler structure. A square systolic array is developed to implement the Levinson- and Schur-type temporal recursive LS (RLS) algorithms. A highly concurrent architecture which exploits the parallelism of the spatial Schur-type recursions is illustrated to perform the LDU decomposition of the cross-correlation matrix