Image Compression Using LBG Algorithm with Histogram Visualization

This documentation demonstrates how to compress images using the LBG (Linde-Buzo-Gray) algorithm while displaying the image histogram. The LBG algorithm is a widely-used vector quantization technique that effectively reduces image file size while preserving key visual characteristics and quality. The implementation typically involves initializing codebook vectors, performing iterative clustering using K-means methodology, and calculating distortion measures until convergence. Through the LBG algorithm, we compress redundant image information by replacing groups of similar pixels with representative code vectors, thereby optimizing storage space and improving image transmission efficiency. The algorithm operates by partitioning the image into blocks, computing centroid values, and minimizing mean squared error between original and compressed blocks. Additionally, we visualize the image histogram to analyze pixel distribution patterns. The histogram provides critical insights into luminance and color distribution characteristics, enabling deeper image analysis and understanding. Histogram computation involves creating frequency distributions of pixel intensities across different color channels, which can be implemented using binning operations and cumulative distribution functions. This guide will help you better understand and apply the LBG algorithm alongside histogram analysis for comprehensive image processing tasks. Code implementation typically requires functions for image preprocessing, codebook generation, vector quantization, and histogram equalization techniques.