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Lossy to lossless image compression based on reversible integer DCT

Lossy to lossless image compression based on reversible inte...

引用

IEEE International Conference on image processing

作者： Lei Wang Jiaji Wu Licheng Jiao Li Zhang Guangming Shi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A progressive image compression scheme is investigated using reversible integer discrete cosine transform (RDCT) which is derived from the matrix factorization theory. Previous techniques based on DCT suffer from bad performance in lossy image compression compared with wavelet image codec. And lossless compression methods such as IntDCT, I2I-DCT and so on could not compare with JPEG-LS or integer discrete wavelet transform (DWT) based codec. In this paper, lossy to lossless image compression can be implemented by our proposed scheme which consists of RDCT, coefficients reorganization, bit plane encoding, and reversible integer pre- and post-filters. Simulation results show that our method is competitive against JPEG-LS and JPEG2000 in lossless compression. Moreover, our method outperforms JPEG2000 (reversible 5/3 filter) for lossy compression, and the performance is even comparable with JPEG2000 which adopted irreversible 9/7 floating-point filter (9/7F filter).

关键词： image coding Discrete cosine transforms Transform coding Filters Performance loss Discrete wavelet transforms Codecs image reconstruction Laboratories Information processing

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image Denoising with Non-Local Means in the Shearlet Domain

Image Denoising with Non-Local Means in the Shearlet Domain

引用

International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping

作者： Xiaohua Zhang Qiang Zhang L. C. Jiao Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this paper, a novel method for image denoising is proposed which adopts multiscale geometry tool. Firstly the image is decomposed by discrete shearlet transform. The shearlet coefficients of each direction approach the generalized Gaussian distribution. We use the principal component analysis (PCA) for every similarity window of shearlet coefficients. Then we use Generalized Gaussian model of non-local means method to handle the shearlet coefficients. Finally, we reconstruct image with the new shearlet coefficients to obtain the result. Numerical results show that our algorithm competes favorably with nonlocal means algorithms in the case of high noise.

关键词： image denoising Transforms Pixel Principal component analysis Algorithm design and analysis Noise reduction Noise

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Design RF diplexer by directional immune clonal selection algorithm

Design RF diplexer by directional immune clonal selection al...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Qiuyi Wu Licheng Jiao Yangyang Li Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Based on the clonal selection theory of the immune system, a novel optimal algorithm, called a directional immune clonal selection algorithm (DICSA), is proposed to design RF diplexer. In DICSA, antibody is proliferated and divided into a set of subpopulation groups. In the antibody's updating, the clonal mutation is applied to avoid premature convergences. The clonal selection realizes the information communication between the subpopulation groups so as to improve the search efficiency. DICSA is applied to a practical problem, designing RF diplexer. The results show that DICSA has stable performance during the optimization process with a satisfactory result.

关键词： Algorithm design and analysis Radio frequency Filters Laboratories Information processing Design optimization Circuits Artificial intelligence Artificial immune systems Least squares methods

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An analysis of the eigenvalues method for polarimetric SAR image classification

An analysis of the eigenvalues method for polarimetric SAR i...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： S. P. Gou Xin. Qiao L. C. Jiao Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this paper, we focus on the distribution of eigenvalues, and based on Gaussian assumption, then we do an analysis of the eigenvalues potential for POL-SAR classification. Generally, we use Gaussian mixture model to describe the distribution of the eigenvalue and Bayesian classifier to achieve the POL-SAR pixel classification. The method is tested with the NASA/JPL AIRSAR data.

关键词： Decision support systems

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Optimizing detector distribution in V-detector negative selection using a constrained multiobjective immune algorithm

Optimizing detector distribution in V-detector negative sele...

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Congress on Evolutionary Computation

作者： Fang Liu Maoguo Gong Jingjing Ma Licheng Jiao Wei Zhang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this paper, a novel constrained multiobjective immune algorithm for optimizing detector distribution in V-detector negative selection is proposed. The theory of artificial immune system (AIS) and the spirit of population evolution are introduced to generate detectors. By combining the constraint handling technique and AIS-based multiobjective optimization, the algorithm is able to steadily maximize the anomaly coverage with little extra cost, which means the distribution with maximized coverage of the non-self space and minimized overlapping among detectors with fixed size will be well realized. Furthermore, the new approach is tested on some benchmark problems. The experimental results show that compared with some state-of-the-art methods, our algorithm can remarkably outperform them in terms of enhancing the detection rate by optimizing distribution without increasing the number of detectors.

关键词： Detectors Optimization Immune system Testing Shape Training data Aerospace electronics

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Hybrid Immune Algorithm with intelligent recombination

Hybrid Immune Algorithm with intelligent recombination

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Congress on Evolutionary Computation

作者： Maoguo Gong Licheng Jiao Wenping Ma Ronghua Shang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this study, we introduce a hybrid immune algorithm based on the intelligent recombination operator and clonal selection algorithm. The intelligent recombination operator uses orthogonal experimental design for factor analysis which identifies the potential gene segments from two individuals to improve their antigenic affinities. The new algorithm, termed as Hybrid Immune Algorithm with Recombination (HIAR), can avoid the decrease of gene diversity in evolutionary process. It evaluates the hamming distance before recombination and uses the two individuals which have the largest hamming distance between each other to implement intelligent recombination operator. It is shown empirically that HIAR has better performance in solving benchmark functions as compared with Intelligent Evolutionary Algorithm and Clonal Selection Algorithm.

关键词： Immune system Competitive intelligence Design for experiments Hamming distance Computational intelligence Approximation algorithms Design optimization Algorithm design and analysis Evolutionary computation Protection

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Isomerous multiple classifier ensemble method with SVM and KMP

Isomerous multiple classifier ensemble method with SVM and K...

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International Conference on Audio, Language and image processing, ICALIP

作者： Shuiping Gou Shasha Mao Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A method for multi-classifier ensemble of Support Vector Machine ensemble (SVMs) and Kernel Matching Pursuit Ensemble (KMPs) is proposed. Support Vector Machine has advantage in solving classification problem of high dimension and small size dataset, and Kernel Matching Pursuit has almost classified performance and the more sparsely solution as comprised with the SVM. So the SVM and the KMP are mix boosted in this paper, which can decrease generalization errors of the single classifier ensemble and improve ensemble classification accuracy by increasing diversity between ensemble individuals. The experiments show that the proposed method can shorten running time and improve classification accuracy compared with individual SVMs or KMPs.

关键词： Support vector machines Classification algorithms Accuracy Kernel Training Matching pursuit algorithms Machine learning

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Hybrid multiobjective estimation of distribution algorithm by local linear embedding and an immune inspired algorithm

Hybrid multiobjective estimation of distribution algorithm b...

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Congress on Evolutionary Computation

作者： Dongdong Yang Licheng Jiao Maoguo Gong Hongxiao Feng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A novel hybrid multiobjective estimation of distribution algorithm is proposed in this study. It combines an estimation of distribution algorithm based on local linear embedding and an immune inspired algorithm. Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise continuous (m-1)-dimensional manifold, where m is the number of objectives. By this regularity, a local linear embedding based manifold algorithm is introduced to build the distribution model of promising solutions. Besides, for enhancing local search ability of the EDA, an immune inspired sparse individual clone algorithm (SICA) is introduced and combined with the EDA. The novel hybrid multiobjective algorithm, named HMEDA, is proposed accordingly. Compared with three other state-of-the-art multiobjective algorithms, this hybrid algorithm achieves comparable results in terms of convergence and diversity. Besides, the tradeoff proportions of EDA to SICA in HMEDA are studied. Finally, the scalability to the number of decision variables of HMEDA is investigated too.

关键词： Electronic design automation and methodology Machine learning algorithms Probability distribution Evolutionary computation Cloning Hybrid power systems Genetic mutations Genetic algorithms Sampling methods Principal component analysis

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SAR image Despeckling via Spatially Constrained Multiscale Products

SAR Image Despeckling via Spatially Constrained Multiscale P...

引用

2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

作者： X. L. Tian L. C. Jiao X. H. Zhang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

This paper presents a wavelet-based multiscale products scheme for synthetic aperture radar (SAR) image despeckling. A compactly supported quadratic spline function that approximates the first derivative of Gaussian is employed in the scheme to decompose log-transformed SAR images. The multiplied results of the decomposed coefficients of adjacent scales consist of multiscale products. The multiscale products can sharp the important structures while weakening noise. A spatially selective neighborhood technique by iteratively selecting neighborhood system in the multiscale products is introduced in searching the important structure information. The influence of the spatial information is imposed on the multiscale products, instead of on the wavelet coefficients, which improves the capability of identifying important features. Experiments show that the proposed scheme is better in SAR image despeckling and preserving edges and detail information than other waveletbased multiscale products methods.

关键词： Despeckling synthetic aperture radar multiscale products wavelet transform neighborhood system

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Medical image segmentation based on immune clonal optimization

Medical image segmentation based on immune clonal optimizati...

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IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS)

作者： Wenping Ma Licheng Jiao Ronghua Shang Fujia Zhao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

ISBN: (纸本)9781424447374;9781424447541

Based on the clonal selection theory of artificial immune system, a novel optimal entropy threshold medical image segmentation method is proposed, in which, the affinity function is the optimal entropy threshold, and the medical image segmentation is considered as a optimization problem, clonal operator effectively enlarges searching range, supplies the diversity of solutions and can find the optimal threshold. This paper applies new algorithm to the challenging application: gray matter/white matter segmentation in MRI images, the algorithm is depicted in detail and the convergence is proven, the performance and computational complexity of the algorithm are described by quantitative analysis. Experiment results demonstrate the potential of the algorithm for medical image segmentation.

关键词： Biomedical imaging image segmentation Immune system Entropy Artificial immune systems Optimization methods Magnetic resonance imaging Computational complexity image analysis Performance analysis

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