An Improved Codebook Design for 3D-MIMO Multiuser Precoding Scheme
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Linde–Buzo–Gray algorithm
Zero-forcing precoding
In vector quantization(VQ),the initial codebook design influences or determines the times of iteration of codebook training method and the quality of codebook.So it is very important for VQ codebook.For existing initial codebook algorithms,the quality of initial codebook is strongly influenced by the initial codewords selected from the training vectors and initial codebook is difficult to match with the features of the training vectors.To overcome these disadvantages,an algorithm,which sorts training vectors according to the sum of each training vector components,divides the sorting vectors to some separating area,calculates the mean of the vectors of each area to obtain the initial codewords,is proposed.Because of the use of vector feature,the proposed algorithm doesn't depend on the structures of images and produces a robust initial codebook.Experimental results show that the proposed algorithm is a better algorithm.The proposed method can be combined with the LBG algorithm to further improve the quality of codebook.
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In vector quantization(VQ),the initial codebook design is very important for VQ codebook performances.To overcome disadvantages of existing initial codebook algorithms,a new separating mean algorithm for learning vector quantization(LVQ)based upon self-organizing feature maps(SOM) was proposed.Experimental results for image VQ show that new initial codebook algorithm is better than random and splitting algorithm.
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Learning vector quantization
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A codebook design algorithm for vector quantization based on particle swarm optimization(PSO)is proposed. To solve the shortcoming that the initial codebook selection affects the convergence speed of codebook training and the per- formance of the final codebook,the initial codebook with global characteristics is produced using PSO,and iteratively pro- duce final codebook with local characteristics using Linde-Buzo-Gray(LBG).Both of the theory and practical effect show the rationality of the proposed algorithm.
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Learning vector quantization
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The codebook design of vector quantization (VQ) based on a training set is computationally very expensive due to a lot of distance computations in the process of its clustering. In order to speed up the process of VQ codebook design, a fast algorithm is proposed in this paper. The proposed algorithm puts the training vectors into an orderly whole according to the characteristic values of training vectors. An ordered initial codebook is got from the ordered training sets. The clustering process of VQ is speed up by employing the fast kick-out conditions in the ordered codebook. Experimental results confirmed that the proposed method can speed up the design process and improve the codebook performance.
Linde–Buzo–Gray algorithm
Learning vector quantization
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Design of the codebook is of great importance in the vector quantization,and most algorithms of designing codebook are based on the initial codebook.From the drawbacks of the classical LBG algorithm,a novel and efficient generation algorithm of the initial codebook based on the fuzzy clustering theory was presented.With this algorithm,the code vectors of the initial codebook could be dispatched well in the vector space,and the area,whose input probability density was larger,was occupied.After that,the LBG algorithm could avoid being trapped in the local optimization and the codebook would be in better performance and closer to the global optimization with faster convergent speed as well as fewer times of iteration.This novel algorithm was applied to the image coding experiment,and the result shows that it can enhance the performance of the vector quantization,both in efficiency and quality.
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Vector Quantization is lossy data compression technique and has various applications. Key to Vector Quantization is good codebook. Once the codebook size is fixed then for any codebook generation algorithm the MSE reaches a value beyond which it cannot be reduced unless the codebook size is increase. In this paper we are proposing bi-level codebook generation algorithm which reduces mean squared error (MSE) for the same codebook size. For demonstration we have used codebooks obtained from well known Linde Buzo and Gray (LBG) algorithm. The proposed method is general and can be applied to any codebook generation algorithm.
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A vital step in building a vector quantizer is to generate an optimal codebook. Among the algorithms presented in the literature, the Maximum Descent (MD) algorithm appears to be a promising alternative codevector generation technique to the generalized Lloyd (LBG) algorithm, when dealing with vector quantization of images. In this paper, a novel vector quantization codebook generation approach is presented. The algorithm uses an MD codebook as an initial codebook and a compression of this codebook is then achieved based on a simple feature clustering technique. According to this technique, we attempt to arrange the codevectors of the MD codebook in a way that prefixed number of clusters results. The centroids of the resulted clusters form a reduced MD codebook. Using this new technique we can produce codebooks with about 0.2 - 0.6 db improvement in peak-signal to noise ratio and a reduction of 10% - 20% in the codebook size compared to the LBG algorithm.
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This paper presents a fast algorithm to find an optimal subset codebook from a super codebook in a way that the RMS error between the new codebook and the training set in vector quantization becomes minimum. To have a fast algorithm, a genetic based algorithm is used that uses 2 evolutions, one in designing the whole sub-codebook and the other in finding each individual codeword of the sub-codebook. This optimal codebook can be efficiently used in the real time compression of the images with PSNR improvement of about 1.1 - 8.2db in blocks of the image.
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The sorted separated means algorithm is based.To get a better codebook,it improved on the case of empty cells.It put the input vector which had the longest distance to the current codebook into the empty cells after the initial codebook is got.The algorithm improves the LBG algorithm and the sorted separated means algorithm.The codebook generated by the proposed algorithm is closer to global optimal.The validity is proved by the simulation result.
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Linde–Buzo–Gray algorithm
Learning vector quantization
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