Comments on "modified K-means algorithm for vector quantizer design"

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• Comments on " Modified K - means algorithm for vector quantizer design

  Scholarly record2017Kuldip K. Paliwal, V. Ramasubramanian

  Recently a modified -means algorithm for vector quantization design has been proposed where the codevector updating step is as follows: new codevector = current codevector + scale factor (new centroid current codevector). This algorithm uses a fixed value for the scale factor. In

• Comments on “ Modified - Means Algorithm for Vector Quantizer Design ”

  Scholarly record2000Kuldip K. Paliwal, V. Ramasubramanian

  Recently a modified -means algorithm for vector quantization design has been proposed where the codevector updating step is as follows: new codevector = current codevector + scale factor (new centroid current codevector). This algorithm uses a fixed value for the scale factor. In

• Vector Quantizer Design Using Genetic Algorithms

  Scholarly record1996Data Compression ConferenceSunghyun Choi, Wee Keong Ng

  The design of vector quantizers (VQs) that yield minimal distortion is one of the most challenging problems in source coding. The problem of VQ design is to find a codebook that gives the least overall distortion (or equivalently, the largest signal-to-noise ratio (SNR)) for a gi

• A clustering algorithm for entropy-constrained vector quantizer design with applications in coding image pyramids

  Scholarly record1995IEEE Trans. Circuits Syst. Video Techn.Diego P. de Garrido, William A. Pearlman, Weiler Alves Finamore

  {A clustering algorithm for the design of eecient vector quantizers to be followed by entropy coding is proposed. The algorithm, called entropy-constrained pair-wise nearest neighbor (ECPNN), designs codebooks by merging the pair of Voronoi regions which gives the least increase

• An algorithm for uniform vector quantizer design

  Scholarly record1984IEEE Trans. Information TheoryKhalid Sayood, Jerry D. Gibson, Martin C. Rost

  A vector quantizer maps a k-dimensional vector into one of a finite set of output vectors or “points”. Although certain lattices have been shown to have desirable properties for vector quantization applications, there are as yet no algorithms available in the quantization literat

• A fast search algorithm for mean-removed vector quantization using edge and texture strengths of a vector

  Scholarly record2005Image Vision Comput.Jim Z. C. Lai, Yi-Ching Liaw

  In this paper, a fast search algorithm for mean-removed vector quantization is proposed. Two inequalities are used to reduce distortion computations. Our algorithm makes use of a mean-removed vector’s features (edge and texture strengths) to reject many unlikely codewords and it