Implementation of Image Compression Using BP Neural Network

In this article, I will introduce how to implement an image compression program using a BP neural network. The BP (Backpropagation) neural network is a powerful machine learning algorithm that can learn image features through training and perform compression. By utilizing a BP neural network, we can reduce image size while preserving critical image information. This image compression program plays a vital role in image storage and transmission, saving both space and time. Therefore, understanding how to implement image compression using BP neural networks is highly valuable. From a code implementation perspective, the process typically involves several key steps: First, the image data is preprocessed by dividing it into blocks and normalizing pixel values. The neural network architecture usually consists of an input layer matching the block size, a smaller hidden layer (the compression layer), and an output layer matching the input dimensions. The backpropagation algorithm adjusts weights through gradient descent to minimize reconstruction error, where common loss functions include Mean Squared Error (MSE) between original and reconstructed images. Key implementation functions would involve weight initialization, forward propagation calculations, error backpropagation, and weight updates using optimization methods like stochastic gradient descent.