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image quantization using improved artificial fish swarm algorithm

SOFT COMPUTING 2015年第9期19卷 2667-2679页

作者： El-said, Shaimaa Ahmed Zagazig Univ Fac Engn Elect & Commun Dept Zagazig Egypt

Most image compression algorithms suffer from several drawbacks: high-computational complexity, moderate reconstructed picture qualities, and a variable bit rate. In this paper, an efficient color image quantization technique that depends on an optimized Fuzzy C-means (OFCM) algorithm is proposed. It exploits the optimization capability of the improved artificial fish swarm algorithm to overcome the shortage of Fuzzy C-means algorithm. It uses error diffusion algorithms to obtain perceptually better images after quantization. Experiments are carried out to estimate the performance of the proposed OFCM algorithm in image compression using standard image set. The results indicate that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction. The performance efficiency of the proposed technique is compared with those of three other quantization algorithms. The Comparative results confirmed that the OFCM has potential in terms of both accuracy and perceptual quality as compared to recent methods of the literature.

关键词： image quantization Compression Data clustering FCM Swarm intelligence Artificial fish swarm algorithm (AFSA)

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image quantization using reaction-diffusion equations

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SIAM JOURNAL ON APPLIED MATHEMATICS 1997年第1期57卷 153-175页

作者： Alvarez, L Esclarin, J UNIV LAS PALMAS GRAN CANARIA DEPT INFORMAT & SISTEMAS CAMPUS TAFIRA LAS PALMAS SPAIN

In this paper we present an image quantization model based on a reaction-diffusion partial differential equation. The quantized image is given by the asymptotic state of this equation. Existence and uniqueness of the solution are proved in the framework of viscosity solutions. We introduce an L(infinity) stable algorithm in order to compute numerically the solution of the equation, and some experimental results are shown. A new energy functional based on the classical Lloyd method is used to compute the quantizer codewords.

关键词： image quantization partial differential equation viscosity solutions multiscale vision model

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image quantization as a dimensionality reduction procedure in color and texture feature extraction

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NEUROCOMPUTING 2016年第Part2期173卷 385-396页

作者： Ponti, Moacir Nazare, Tiago S. Thume, Gabriela S. Univ Sao Paulo Inst Ciencias Matemat & Comp BR-13566590 Sao Carlos SP Brazil

The image-based visual recognition pipeline includes a step that converts color images into images with a single channel, obtaining a color-quantized image that can be processed by feature extraction methods. In this paper we explore this step in order to produce compact features that can be used in retrieval and classification systems. We show that different quantization methods produce very different results in terms of accuracy. While compared with more complex methods, this procedure allows the feature extraction in order to achieve a significant dimensionality reduction, while preserving or improving system accuracy. The results indicate that quantization simplify images before feature extraction and dimensionality reduction, producing more compact vectors and reducing system complexity. (C) 2015 Elsevier B.V. All rights reserved.

关键词： image quantization LPP Compact features extraction

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image quantization by nonlinear smoothing

Image quantization by nonlinear smoothing

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Conference on Investigative and Trial image Processing

作者： ALVAREZ, L ESCLARIN, J UNIV LAS PALMAS GRAN CANARIA DEPT INFORMATE-35017 LAS PALMASSPAIN

We present a quantization technique based on the partial differential equation (∂u/∂t) = g(||∇(Gσ * u)||) |∇u|div(∇u/|∇u|) + f(u, t) where |&nabl... 详细信息

ISBN: (纸本)0819419265

We present a quantization technique based on the partial differential equation (∂u/∂t) = g(||∇(G_σ * u)||) |∇u|div(∇u/|∇u|) + f(u, t) where |∇u|div(∇u/|∇u|) represents the derivative of the function u in the direction orthogonal to the gradient, G_s is a linear convolution kernel, g is a decreasing function and f(s, t) is a lipschitz function. We assume that when t tends to +∞, f(s,t) tends uniformly to a function f_∞(s) which has a finite number of zeros with negative derivative which act as attractors in the system and represent the quantization levels. The location of the zero-crossing of the function f_∞s(s) depends on the histogram of the initial image given by u₀. We introduce a new energie based in the Lloyd model to compute the quantizer levels. We develop a numerical scheme to discretize the above equation and we present some experimental results.

关键词： image quantization PARTIAL DIFFERENTIAL EQUATIONS MULTISCALE ANALYSIS image PROCESSING LLOYD ENERGY DYNAMIC PROGRAMMING

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COLOR image quantization FOR HIGH-RESOLUTION GRAPHICS DISPLAY

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image AND VISION COMPUTING 1991年第5期9卷 303-312页

作者： GOLDBERG, N Komoy St. 7A Haifa 32761 Israel

Techniques for the display of natural colour images on high resolution graphics displays are described. The high resolution of the display enables the use of a combination of display colours to reproduce an original image colour and so to achieve high quality display even with a low number of display colours. Adaptation of the colour quantization techniques to the case of high resolution display is presented. The quantization algorithms are developed as a generalization of the common vector quantization algorithms.

关键词： image quantization HIGH RESOLUTION GRAPHICS DISPLAY quantization ALGORITHMS

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A study of sine-cosine oscillation heterogeneous PCNN for image quantization

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SOFT COMPUTING 2019年第22期23卷 11967-11978页

作者： Yang, Zhen Lian, Jing Li, Shouliang Guo, Yanan Ma, Yide Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China

A new heterogeneous pulse-coupled neural network (HPCNN) is proposed to prune the boundary effects in image quantization. An oscillating sine-cosine pulse-coupled neural network (SC-PCNN) is combined with the morphological algorithm and two classical PCNNs which have different parameters corresponding to different image regions to form the proposed new HPCNN model (SC-HPCNN). This model retains the natural characteristics of classical PCNN while revealing its own merits;when it is used to accomplish image quantization, the quantization noise and boundary effects are removed dramatically, without significantly degrading image quality. Furthermore, experimental results also show that the proposed model outperforms previous approaches, and it operates in accordance with the characteristics of the human visual system.

关键词： PCNN SC-HPCNN image quantization Boundary effect

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Application of heterogeneous pulse coupled neural network in image quantization

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JOURNAL OF ELECTRONIC IMAGING 2016年第6期25卷 1-11页

作者： Huang, Yi Ma, Yide Li, Shouliang Zhan, Kun Lanzhou Univ Sch Informat Sci & Engn 222 Tianshui Rd Lanzhou 730000 Peoples R China

On the basis of the different strengths of synaptic connections between actual neurons, this paper proposes a heterogeneous pulse coupled neural network (HPCNN) algorithm to perform quantization on images. HPCNNs are developed from traditional pulse coupled neural network (PCNN) models, which have different parameters corresponding to different image regions. This allows pixels of different gray levels to be classified broadly into two categories: background regional and object regional. Moreover, an HPCNN also satisfies human visual characteristics. The parameters of the HPCNN model are calculated automatically according to these categories, and quantized results will be optimal and more suitable for humans to observe. At the same time, the experimental results of natural images from the standard image library show the validity and efficiency of our proposed quantization method. (C) 2016 SPIE and IS&T

关键词： heterogeneous pulse coupled neural networks image quantization human visual system

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Iterative algorithm for image quantization with signal adaptation

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OPTICS COMMUNICATIONS 1998年第1-6期158卷 18-22页

作者： Tuttass, T Zeggel, T Bryngdahl, O Univ Essen Gesamthsch Dept Phys D-45117 Essen Germany

An iterative Fourier transform algorithm (IFTA) is applied for multilevel image quantization. A spatial operator is introduced. based on local signal adaptation, which allows a flexible handling of the quantization noise, especially near the medium quantization level. Examples are shown and comparisons with other iterative Fourier transform algorithms are performed. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： image quantization multilevel iterative Fourier transform algorithm

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Mammalian visual characteristics inspired perceptual image quantization using pulse-coupled neural networks

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OPTIK 2015年第21期126卷 3135-3139页

作者： Du, Songlin Huang, Yi Ma, Jianlin Ma, Yide Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

As a matter of fact, mammalian visual system do not pay an equivalent attention to different regions in an image, the visual cortex is less sensitive to textures than non-textures. Therefore, to obtain the optimal visual quality and the perfect compression ratio simultaneously in image quantization, textures should be quantized coarsely, and non-textures should be quantized finely. The pulse-coupled neural networks (PCNN) is a model of synchronous pulse bursts in mammalian visual cortex, which has been proved to be extremely effective in image processing because of its biological background. In this work, a mammalian visual characteristics inspired perceptual image quantization strategy is proposed. It employs PCNN to extract textures from original image. Then, pixels in textures are quantized into less gray scale layers than pixels in non-textures. After that, quantized textures and quantized non-textures are consolidated. Experimental results prove validity and efficiency of the proposed method. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： image quantization Mammalian visual characteristics (MVC) Pulse-coupled neural network (PCNN) Structural uncertainty

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The hardware-based implementation of the colour palette generation stage of a colour image quantization algorithm

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MICROPROCESSORS AND MICROSYSTEMS 2006年第5期30卷 234-249页

作者： Kurdthongmee, W. Walailak Univ Sch Engn & REsources Div Comp Engn Tha Sa La 80160 Nakorn Si Thamm Thailand

Many techniques have been proposed to quantize a digital colour image in order to reduce the representative number of colours to be suitable for presenting on different type of display screens. Most of the techniques are targeted for implementation on a general purpose multitasking computer with low restriction on resource utilization. The drawback of these techniques relies on the fact that they cannot fulfil the requirement of some applications with real-time constraint and limited resources. In this paper, the most suitable algorithm for hardware implementation is reviewed and the details of its implementation and experimentation which is focused on the colour palette generation stage are presented. The implementation given in this paper is for use in construction of colour palette image compression for real-time transferring of video data to an LED display board. It can also be adapted to use as a dedicated computer hardware for other real-time image compression applications. (C) 2006 Elsevier B.V. All rights reserved.

关键词： palette generation image quantization image compression FPGA-based implementation

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