In this paper, we have described the application of Vector Quantization in Image and Speech processing. Compressing image data by using Vector Quantization (VQ)[1]-[3] will compare Training Vectors with Codebook. The result is an index of position with minimum distortion. Basically there are three ways of generating the code book: 1)The random method, 2)Pair wise nearest neighbor clustering and 3)Splitting. The implementing Random Codebook will reduce the image quality. This paper presents the Splitting solution to implement the Codebook, which improves the image quality by the average Training Vectors, then splits the average result to Codebook that has minimum distortion. The result from this presentation will give the better quality of the image than using Random Codebook.
[...] The result is reducing scatter data better than random sample (Random Codebook) Vector Quantization In Image Compression using Vector Quantization, an input image is divided into small blocks called Training Vectors This Training Vectors can be closely reconstructed from applying a transfer function to a specific region of an input image itself, which is called Codebook ( xˆi Thus, only the set of transfer functions, which have fewer data than an image, were required for reconstruct the input image back. [...]
[...] The areas on the diagram which would represent abrupt intensity changes from one pixel to the next are sparsely populated. n = Split the Codebook to 2 vectors: ^x1(1)+ɛ1 and ^x1(1)-ɛ when ε1 is the average value as shown in figure 2 Ni = J = 1,2,3 Nc FIGURE 2. Distribution of pairs of adjacent pixels from grayscale Lena. FIGURE 4. Vector quantization to 4 bits per 2D-vector. Figure 4 shows how things look with VQ. As in Figure the codebook vectors are represented as big red dots, and the red lines delimit their zones of influence. [...]
[...] The FFT is a powerful tool since it calculates the DFT of an input in a computationally efficient manner, saving processing power and reducing computation time. The DFT is given by the following equation. figure-6. Steps used in MFCC Because of the known variation of the ear's critical bandwidths with frequency, filters spaced linearly at low frequencies and logarithmically at high frequencies have been used to capture the phonetically important characteristics of speech. This result suggested that a compact representation would be provided by a set of Melfrequency cepstrum coefficients. [...]
[...] That becomes data storage in Training Vector and Codebook Vectors region according to the image compression Mathematical Analysis of LBG Algorithm LBG starts with define in the value of Ym, then update Codebook by replacing the value that makes less distortion. This update step will iterate until the distortion is under the LBG limit. LBG instructions will split the image into the same size parts and forms to Training Vectors xj(k) as: xj(1) xj(2) xj(3) xj(k) j = n = number of the image parts, k = vector dimension; Steps of LBG 1. [...]
[...] IEEE, Vol figure-14 Code book of identity claimed by the speaker E u c lid e a n d ia ta n c e figure-15 Euclidean Distance 8. Conclusion The main goal of this paper is the effective utilization of Vector Quantization algorithm in the area of image compression and speech recognition. In the first phase, the fundamental of image compression has been implemented with the vector quantization in which two types of generation of codebook is discussed. While in the second phase, the technique of vector quantization is described which consist of extracting the small [...]
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