MATLAB Implementation of Vector Quantization (VQ)
MATLAB Implementation of Vector Quantization (VQ) with Algorithm Explanations and Code Implementation Details
Implementation of Vector Quantization Using the LBG Algorithm
This graduation project demonstrates the implementation of vector quantization through the LBG algorithm using MATLAB, focusing on code structure, algorithm workflow, and vector dimension reduction techniques.
Vector Quantization Source Code and Images
Vector Quantization with comprehensive documentation, source code implementation, and sample images - a valuable resource for download and reference
MATLAB Implementation of Image Compression and Reconstruction Steps
The MATLAB implementation of image compression and reconstruction involves: ① Performing wavelet decomposition to obtain first-level low-frequency and high-frequency coefficients ② Retaining low-frequency coefficients while compressing high-frequency coefficients using neural network-based vector quantization encoding ③ Restoring high-frequency coefficients from the codebook ④ Reconstructing the image using preserved low-frequency and restored high-frequency coefficients
Speaker Recognition using Vector Quantization (VQ) Method with MATLAB Implementation
MATLAB code for speaker recognition employing Vector Quantization technique, designed for beginners new to vector quantization concepts
Image Compression Using FFT, DCT, HT, Wavelet Transforms and Vector Quantization Techniques
MATLAB implementation of image compression based on Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Hadamard Transform (HT), Wavelet Transform and Vector Quantization techniques. Implementation includes transform domain processing, coefficient quantization, and entropy coding modules.
Implementation of Vector Quantization-Based Speaker Recognition System
Development of a text-independent speaker recognition system using vector quantization (VQ) in MATLAB environment. This system enables real-time identification of speakers from 1-7 second voice recordings through live audio capture. The implementation includes feature extraction using MFCC (Mel-frequency cepstral coefficients), codebook generation via LBG (Linde-Buzo-Gray) algorithm, and pattern matching through distortion measurement. Usage instructions: 1) Launch MATLAB and set Current Directory to VQ folder path (e.g., E:\vq) 2) Execute training commands in Command Window to create speaker models 3) Perform real-time recognition through audio input interface.
Image Compression Using Vector Quantization with MATLAB Implementation
This MATLAB-based code implements image compression through vector quantization, utilizing built-in MATLAB functions for image processing and neural network training. The implementation employs Self-Organizing Map (SOM) neural networks for codebook training, achieving high-quality reconstructed images. The description covers key MATLAB functions, their usage, and the algorithm workflow for effective image compression.
Implementation of Vector Quantization-Based Speaker Recognition System in MATLAB Environment
Implementation of a speaker recognition system using vector quantization in MATLAB environment for real-time identification of 1-second to 7-second speech samples from different speakers, enabling text-independent automatic speaker verification with detailed code implementation procedures and algorithm descriptions.
Vector Quantization Codebook Design Based on LBG Algorithm with MATLAB Implementation
MATLAB program for designing vector quantization codebooks using the LBG algorithm, featuring comprehensive code implementation details and algorithm explanations