Ph.D. Thesis On Vector Quantization

Ph.D. Thesis On Vector Quantization-5
Step 3: Once the mapping of all the input vectors to the initial code vectors is made, compute the centroids of the partition region found in step 2. We used firefly algorithm for vector quantization for LBG scheme.

Step 3: Once the mapping of all the input vectors to the initial code vectors is made, compute the centroids of the partition region found in step 2. We used firefly algorithm for vector quantization for LBG scheme.FFA LBG vector quantization algorithm The basic principle of firefly algorithm is flashing pattern and characteristics of fireflies.

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In vector quantization the input is given as a image after that the codeword and index is generated for that image then transmitted through the channel. The information is received by the decoder which decodes the image parameter and obtained the actual image. Let the iteration counter be m =1 and the initial distortion D1 =1.

LBG Vector Quantization Algorithm It is the most commonly method used for the vector quantization. Step2: Using codebook Cm = , partition the training set into cluster sets Ri using the nearest neighbor condition. If Dm _ Dm 1 The various methods are used for the image compression system optimization.

It is done by finding the nearest group with the data dimensions available, then predicting the result based on the values for the missing dimensions, assuming that they will have the same value as the group's centroid.

For density estimation, the area/volume that is closer to a particular centroid than to any other is inversely proportional to the density (due to the density matching property of the algorithm).

Step 4: Randomly select a codebook and record its fitness value.

If there is a brighter codebook, then it moves toward the brighter codebook (highest fitness value) based on Equations Here Xi is randomly selected codebook, Xj is brighter Codebook, where u is a random number between 0 and 1, k = 1,2,. Vector quantization, also called "block quantization" or "pattern matching quantization" is often used in lossy data compression.It works by encoding values from a multidimensional vector space into a finite set of values from a discrete subspace of lower dimension.Vector quantization is used for lossy data compression, lossy data correction, pattern recognition, density estimation and clustering.Lossy data correction, or prediction, is used to recover data missing from some dimensions.Vector quantization compresses the size of the image by using the optimization algorithm.We can use FFA and ALO algorithm for the image compression system.In this algorithm we are assuming that brightness of a firefly is equal to objective function value. Step 5: If no firefly fitness value is better than the selected firefly then it moves randomly in search space according to Equation (5) Step 6: Repeat step 3 to step 5 until one of the termination criteria is reached.The lower intensity firefly (lower fitness value) moves toward brighter firefly (higher fitness value). The image compression is mandatory to transfer the image over communication medium to save the bandwidth.This topic is discussing the lossy image compression algorithm which is using optimized vector qunatization method.

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