On the systematic development of fast fuzzy vector quantization for grayscale image compression

This paper focus on AC frequency conversion electric drill of automatic feed drilling system and an MOBP neural network is used to control the WOB (weight on bit) and realize the research of constant pressure automatic feed drilling. The same type of high quality wells' drilling parameter is normalized as a network training set. A more effective optimization algorithm called momentum method is used to design a suitable improved BP neural network for automatic feed drilling system. Modular neural network is established by Matlab/Simulink and compared with conventional PID controller and Fuzzy controller. The simulation results show that in the condition of hysteresis, MOBP has better stability, better robustness and smaller steady-state error than conventional PID and Fuzzy control. The application of Neural Network in automatic feed drilling system has a significance of guidance to improve the performance.

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“…We train the neural network with different number of hidden layer unit by sample sets in MATLAB [7][8]. The training result is shown in chair 1.…”

Section: B Design Hidden Layermentioning

confidence: 99%

Research on oil rig automatic feed drilling system based on MOBP neural network

Liu

Wei

2013

2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP)

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“…Dynamic programming is used as an efficient optimization technique for layout optimization of interconnection networks by Tripathy et al [19]. Tsolakis et al [20] presented a fast fuzzy vector quantization technique for compression of gray scale images. Based on a crisp relation, an input block is assigned to more than one code-word following a fuzzy vector quantization method [21].…”

Section: Introductionmentioning

confidence: 99%

Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression

Nag¹

2017

Preprint

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Vector Quantization (VQ) is a popular image compression technique with a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in Vector Quantization. Linde-Buzo-Gray (LBG) is a traditional method of generation of VQ Codebook which results in lower PSNR value. A Codebook affects the quality of image compression, so the choice of an appropriate codebook is a must.Several optimization techniques have been proposed for global codebook generation to enhance the quality of image compression. In this paper, a novel algorithm called IDE-LBG is proposed which uses Improved Differential Evolution Algorithm coupled with LBG for generating optimum VQ Codebooks. The proposed IDE works better than the traditional DE with modifications in the scaling factor and the boundary control mechanism. The IDE generates better solutions by efficient exploration and exploitation of the search space. Then the best optimal solution obtained by the IDE is provided as the initial Codebook for the LBG. This approach produces an efficient Codebook with less computational time and the consequences include excellent PSNR values and superior quality reconstructed images. It is observed that the proposed IDE-LBG find better VQ Codebooks as compared to IPSO-LBG, BA-LBG and FA-LBG.

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“…The prevailing algorithm for codebook design is Linde-Buzo-Gray (LBG) [20], also known as Generalized Lloyd Algorithm (GLA) or K - means . Other examples of codebook design algorithms are: fuzzy [7,21,22], competitive learning [23], memetic [24], genetic [25], firefly [26] and honey bee mating optimization [27]. …”

Section: Introductionmentioning

confidence: 99%

Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design

Mata

Bandeira

Neto

et al. 2016

Sensors

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The performance of signal processing systems based on vector quantization depends on codebook design. In the image compression scenario, the quality of the reconstructed images depends on the codebooks used. In this paper, alternatives are proposed for accelerating families of fuzzy K-means algorithms for codebook design. The acceleration is obtained by reducing the number of iterations of the algorithms and applying efficient nearest neighbor search techniques. Simulation results concerning image vector quantization have shown that the acceleration obtained so far does not decrease the quality of the reconstructed images. Codebook design time savings up to about 40% are obtained by the accelerated versions with respect to the original versions of the algorithms.

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On the systematic development of fast fuzzy vector quantization for grayscale image compression

Cited by 20 publications

References 28 publications

Research on oil rig automatic feed drilling system based on MOBP neural network

Research on oil rig automatic feed drilling system based on MOBP neural network

Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression

Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design

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