Shengli Xie

Edge-Based Structural Similarity for Image Quality Assessment

Objective quality assessment has been widely used in image processing for decades and many researchers have been studying the objective quality assessment method based on human visual system (HVS). Recently the structural similarity (SSIM) is proposed, under the assumption that the HVS is highly adapted for extracting structural information from a scene, and simulation results have proved that it is better than PSNR (or MSE). By deeply studying the SSIM, we find it fails in measuring the badly blurred images. Based on this, we develop an improved method which is called edge-based structural similarity (ESSIM). Experiment results show that ESSIM is more consistent with HVS than SSIM and PSNR especially for the blurred images

Gradient-Based Structural Similarity for Image Quality Assessment

Objective quality assessment has been widely used in image processing for decades and many researchers have been studying the objective quality assessment method based on human visual system (HVS). Recently the structural similarity (SSIM) is proposed, under the assumption that the HVS is highly adapted for extracting structural information from a scene, and simulation results have proved that it is better than PSNR (or MSE), By deeply studying the SSIM, we find it fails in measuring the badly blurred images. Based on this, we develop an improved method which is called gradient-based structural similarity (GSSIM). Experiment results show that GSSIM is more consistent with HVS than SSIM and PSNR especially for blurred images.

An ant colony optimization algorithm for image edge detection

Ant colony optimization (ACO) is an optimization algorithm inspired by the natural behavior of ant species that ants deposit pheromone on the ground for foraging. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed ACO-based edge detection approach is able to establish a pheromone matrix that represents the edge information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of these ants are driven by the local variation of the imagepsilas intensity values. Experimental results are provided to demonstrate the superior performance of the proposed approach.

Online Blind Source Separation Using Incremental Nonnegative Matrix Factorization With Volume Constraint

Online blind source separation (BSS) is proposed to overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, the existing online BSS methods are mainly used to separate independent or uncorrelated sources. Recently, nonnegative matrix factorization (NMF) shows great potential to separate the correlative sources, where some constraints are often imposed to overcome the non-uniqueness of the factorization. In this paper, an incremental NMF with volume constraint is derived and utilized for solving online BSS. The volume constraint to the mixing matrix enhances the identifiability of the sources, while the incremental learning mode reduces the computational cost. The proposed method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources. Simulations in BSS for dual-energy X-ray images, online encrypted speech signals, and high correlative face images show the validity of the proposed method.

Mixing Matrix Estimation From Sparse Mixtures With Unknown Number of Sources

In blind source separation, many methods have been proposed to estimate the mixing matrix by exploiting sparsity. However, they often need to know the source number a priori, which is very inconvenient in practice. In this paper, a new method, namely nonlinear projection and column masking (NPCM), is proposed to estimate the mixing matrix. A major advantage of NPCM is that it does not need any knowledge of the source number. In NPCM, the objective function is based on a nonlinear projection and its maxima just correspond to the columns of the mixing matrix. Thus a column can be estimated first by locating a maximum and then deflated by a masking operation. This procedure is repeated until the evaluation of the objective function decreases to zero dramatically. Thus the mixing matrix and the number of sources are estimated simultaneously. Because the masking procedure may result in some small and useless local maxima, particle swarm optimization (PSO) is introduced to optimize the objective function. Feasibility and efficiency of PSO are also discussed. Comparative experimental results show the efficiency of NPCM, especially in the cases where the number of sources is unknown and the sources are relatively less sparse.

A new rate-distortion optimization using structural information in h.264 i-frame encoder

Rate-distortion optimization is the key technique in video coding standards to efficiently determine a set of coding parameters. In the R-D optimization for H.264 I-frame encoder, the distortion (D) is measured as the sum of the squared differences (SSD) between the reconstructed and the original blocks, which is same as MSE. Recently, a new image measurement called Structural Similarity (SSIM) based on the degradation of structural information was brought forward. It is proved that the SSIM can provide a better approximation to the perceived image distortion than the currently used PSNR (or MSE). In this paper, a new rate-distortion optimization for H.264 I-frame encoder using SSIM as the distortion metric is proposed. Experiment results show that the proposed algorithm can reduced 2.2~6.45% bit rate while maintaining the perceptual quality.

Improved best prediction mode(s) selection methods based on structural similarity in H.264 I-frame encoder

In H.264 I-frame encoder, the best infra prediction modes are chosen by utilizing the rate-distortion (R-D) optimization whose distortion is the sum of the squared differences (SSD, means the same as MSE) between the reconstructed and the original blocks. Recently a new image measurement called structural similarity (SSIM) based on the degradation of structural information was brought forward. It is proved that the SSIM can provide a better approximation to the perceived image distortion than the currently used PSNR (or MSE). In this paper, we propose two improved prediction modes selection methods based on SSIM for H.264 I-frame encoder. The first one is the SSIM-based R-D optimization (SBRDO) method, the other is the fast mode selection method based on SSIM (FMSBS). Experiments show that both the proposed method can improve the coding efficiency while maintaining the same perceptual reconstructed image quality.

Nonnegative Matrix Factorization Applied to Nonlinear Speech and Image Cryptosystems

Nonnegative matrix factorization (NMF) is widely used in signal separation and image compression. Motivated by its successful applications, we propose a new cryptosystem based on NMF, where the nonlinear mixing (NLM) model with a strong noise is introduced for encryption and NMF is used for decryption. The security of the cryptosystem relies on following two facts: 1) the constructed multivariable nonlinear function is not invertible; 2) the process of NMF is unilateral, if the inverse matrix of the constructed linear mixing matrix is not nonnegative. Comparing with Lins method (2006) that is a theoretical scheme using one-time padding in the cryptosystem, our cipher can be used repeatedly for the practical request, i.e., multitme padding is used in our cryptosystem. Also, there is no restriction on statistical characteristics of the ciphers and the plaintexts. Thus, more signals can be processed (successfully encrypted and decrypted), no matter they are correlative, sparse, or Gaussian. Furthermore, instead of the number of zero-crossing-based method that is often unstable in encryption and decryption, an improved method based on the kurtosis of the signals is introduced to solve permutation ambiguities in waveform reconstruction. Simulations are given to illustrate security and availability of our cryptosystem.

On the kernel function selection of nonlocal filtering for image denoising

Nonlocal filtering has been proved to yield attractive performance for removing additive Gaussian noise from the image by replacing the intensity value of each pixel via a weighted average of that of the full image. The key challenge of the nonlocal filtering is to establish the kernel function for computing the above-mentioned weighting factors, which control the quality of the denoised image result. In contrast to that the exponential function is used in the conventional nonlocal filtering, several new kernel functions are proposed in this paper to be further incorporated into the conventional nonlocal filtering framework to develop new filters. Extensive experiments are conducted to demonstrate not only that the kernel function is essential to control the performance of the algorithm, but also that the new kernel functions proposed in this paper yield superior performance to that of the conventional nonlocal filtering.

Nonorthogonal Approximate Joint Diagonalization With Well-Conditioned Diagonalizers

To make the results reasonable, existing joint diagonalization algorithms have imposed a variety of constraints on diagonalizers. Actually, those constraints can be imposed uniformly by minimizing the condition number of diagonalizers. Motivated by this, the approximate joint diagonalization problem is reviewed as a multiobjective optimization problem for the first time. Based on this, a new algorithm for nonorthogonal joint diagonalization is developed. The new algorithm yields diagonalizers which not only minimize the diagonalization error but also have as small condition numbers as possible. Meanwhile, degenerate solutions are avoided strictly. Besides, the new algorithm imposes few restrictions on the target set of matrices to be diagonalized, which makes it widely applicable. Primary results on convergence are presented and we also show that, for exactly jointly diagonalizable sets, no local minima exist and the solutions are unique under mild conditions. Extensive numerical simulations illustrate the performance of the algorithm and provide comparison with other leading diagonalization methods. The practical use of our algorithm is shown for blind source separation (BSS) problems, especially when ill-conditioned mixing matrices are involved.