Multidimensional Systems and Signal Processing

A low-complexity algorithm is presented for the estimation of the nominal direction-of-arrivals (DOAs) of incoherently distributed (ID) sources. The presented algorithm estimates the nominal DOAs of ID sources by a novel propagator method which makes use of the approximate rotational invariance relationship between two closely spaced identical uniform linear arrays. Without any search and the eigendecomposition of the sample covariance matrix, our algorithm can provide lower computational complexity than other known methods. In addition, it can be applied to the multisource scenario with different angular distribution shapes. Simulation results prove the effectiveness of the presented algorithm.

Stereoscopic imaging is becoming very popular and its deployment by means of photography, television, cinema. . .is rapidly increasing. Obviously, the access to this type of images imposes the use of compression and transmission that may generate artifacts of different natures. Consequently, it is important to have appropriate tools to measure the quality of stereoscopic content. Several studies tried to extend well-known metrics, such as the PSNR or SSIM, to 3D. However, the results are not as good as for 2D images and it becomes important to have metrics dealing with 3D perception. In this work, we propose a full reference metric for quality assessment of stereoscopic images based on the binocular fusion process characterizing the 3D human perception. The main idea consists of the development of a model allowing to reproduce the binocular signal generated by simple and complex cells, and to estimate the associated binocular energy. The difference of binocular energy has shown a high correlation with the human judgement for different impairments and is used to build the Binocular Energy Quality Metric (BEQM). Extensive experiments demonstrated the performance of the BEQM with regards to literature.

The emerging international standard for high efficiency video coding (HEVC) based 3D video coding (3D-HEVC) is an extension of HEVC. In the test model of 3D-HEVC, variable size motion estimation (ME) and disparity estimation (DE) are both employed to select the best coding mode for each treeblock in the encoding process. This technique achieves the highest possible coding efficiency, but it brings extremely high computational complexity which limits 3D-HEVC from practical applications. In this paper, a fast ME/DE algorithm based on inter-view and spatial correlations is proposed to reduce 3D-HEVC computational complexity. Since the multi-view videos represent the same scene with similar characteristic, there is a high correlation among the coding information from inter-view prediction. Besides, the homogeneous regions in texture video have a strong spatial correlation, and thus spatially neighboring treeblocks have similar coding information. Therefore, we can determine ME search range and skip some specific ME and DE rarely used in the previously coded view frames and spatially neighboring coding unit. Experimental results demonstrate that the proposed algorithm can significantly reduce computational complexity of 3D-HEVC encoding while maintaining almost the same rate-distortion performance.

The recent growth of World Wide Web (WWW) and development of the next-generation internet facilitates a huge amount of data being conveniently transmitted via wireless networks. The sensitive information transmitted is potentially vulnerable in the communication channel like wireless networks. Unauthorized users could potentially intercept and negatively exploit the true intent of the information being exchanged between legitimate users. The efficient steganography techniques are very useful to prevent such undesirable interception of information. In this work, we propose and evaluate an efficient image steganography using Fruit Fly Optimization hybridized Improved Seeker (FOIS) algorithm. The FOIS provides information security and safeguards the medical data to avoid medical related cybercrimes. FOIS efficiently determines the optimal locations of pixels adaptively in the spatial domain of the cover image. Initially, the cover image is divided into n blocks of $$8 \times 8$$ , on which a permutation combination is applied to find the number of blocks for further processing. This method improves the image quality and secures data. The secret messages are embedded in each block using optimal pixels selection and Least Significant Bit (LSB) of Discrete Cosine Transform coefficients. Moreover, in order to ensure seamless communication over an insecure communication channel, a dual cryptosystem model is developed which consist of the proposed steganography scheme and Rivest Cipher (RC4) cryptosystem. This work validates the security level of the stego image, and finally the performance is compared with state-of-the-art methods such as LSB, Particle Swarm Optimization and Genetic Algorithm. The performance assessment reveals that the proposed steganography model outperforms other optimization based approaches in terms of Peak Signal-to-Noise Ratio, embedding capacity and imperceptibility.

The Morlet wavelets transform (MWT) is an efficient means of detecting and analyzing transient signals. However, ordinary iterative processes that calculate the MWT are time-consuming. In addition, when the MWT is applied to construct a wavelet power spectrum on a linear frequency axis, the peak response appears at a value lower than the actual signal frequency. In this work, formulae that produce a fast MWT and Morlet power spectrum (MPS) scheme without iterative processes are derived. Also, we discuss in detail why the frequency slant phenomenon occurs. To avert this phenomenon, the transform kernel of the MWT is modified to facilitate the construction of an equal-amplitude Morlet wavelet transform. The modified Morlet power spectrum produces the peak responses roughly proportional to the squared input amplitudes at the accurate signal component frequencies.

The multi-least-squares fitting and regularization formulations of the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) problem are herein presented to provide a new framework for developing the closed-form multiple invariance ESPRIT (MI-ESPRIT) algorithm. The resulting two closed-form MI-ESPRIT algorithms incorporate all invariances in arrays composed of several (more than one) equally translated identical subarrays, and maintain the computational advantage of the standard single invariance ESPRIT.

In this paper, we present a new approach for wideband direction of arrival (DOA) estimation incorporating derivative constraints into the optimization problem. The Taylor series expansion of steering vector with respect to frequency exhibits the dependence of sensor snapshots on the derivatives of the array manifold matrix, suppression of which reduces the data to follow the narrowband data model. Two new formulations following a beamformer framework are developed based on this observation. While the first formulation estimates accurate source directions with apriori knowledge of the signal subspace dimension, the second formulation provides DOA estimates without estimating the number of sources. The coherent averaging at the reference frequency enables the two formulations to handle coherent sources. The effectiveness of the proposed formulations is illustrated with simulations.

The use of a massively parallel neural array for multiple 2-D object recognition is explored. The array architecture has a parallel modular form with each module being trained over a specific object class. One test bed is developed using alphabetic characters which have been subjected to a scale factor and rotational operations. This test bed provides a simultaneous measure of geometric invariance and of character recognition. The performance of the modular design is benchmarked against a backprop-trained multilayer perceptron network of equivalent generality. A second test of the modular array is conducted using TV and FLIR images. This second evaluation assesses the ability to extract obejct signatures from a clutter background.

Chain codes [Freeman 1974; Charavarty 1982; Kim, Lee and Kim 1988; Morrin 1976; Pavlidis 1978] and y-axis representation [Merrill 1973] are both commonly used representation of a region in a binary image. The y-axis representation is very efficient for some geometric applications [Chang and Leu 1990] and morphological operations [Ji, Piper and Tang 1989]. However, it is not efficient to compute the properties on the contour. In this paper, a new and simple algorithm other than Kim's [Kim, Lee and Kim 1988] to convert the y-axis representation to a sequence of chain codes is developed.