Image Compression Using Vector Quantization with MATLAB Implementation

In this article, we provide a comprehensive guide to implementing image compression using vector quantization within the MATLAB development environment. We will not only introduce the essential built-in MATLAB functions required for this process but also delve into their specific usage methods and functionalities. The implementation includes detailed explanations of key functions such as imread() for image input, rgb2gray() for color space conversion, and blockproc() for dividing images into vector blocks. For codebook training, we employ Self-Organizing Map (SOM) neural networks using the selforgmap() function, which efficiently clusters similar image vectors. The compression algorithm works by mapping similar image blocks to representative code vectors, significantly reducing storage requirements. During reconstruction, we use codebook indexing and vector replacement techniques to rebuild the image. Through quantitative metrics like PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) evaluations, we demonstrate that our approach maintains excellent reconstructed image quality while achieving substantial compression ratios. We will thoroughly explain the mathematical foundations of vector quantization and the training parameters that contribute to these optimal results.