Neural Network-Based Image Compression Implementation

This is the code implementation for image compression utilizing neural networks. In this project, we employ deep learning techniques to achieve efficient image compression and decompression. By leveraging the powerful pattern recognition and feature extraction capabilities of neural networks, we can effectively compress redundant information in images, thereby reducing file sizes. This approach not only conserves storage space but also accelerates image transmission speeds. Our implementation uses Convolutional Neural Networks (CNN) to construct the image compression model, trained and optimized with extensive image datasets. Through continuous adjustment of model parameters and architecture, we progressively enhance both compression quality and efficiency. The core algorithm involves encoding images into compact latent representations through convolutional layers and pooling operations, then reconstructing them via deconvolutional layers. Additionally, we research and apply other relevant deep learning techniques such as autoencoders and Generative Adversarial Networks (GANs) to further improve the performance of our image compression algorithm. Autoencoders help learn efficient latent representations through bottleneck architectures, while GANs can generate higher-quality reconstructed images through adversarial training. Key functions include: - Preprocessing modules for image normalization and patch extraction - Encoder networks with convolutional layers for feature compression - Decoder networks with upsampling layers for image reconstruction - Loss functions combining MSE and perceptual metrics for quality preservation Through this project, we aim to provide a more efficient and reliable image compression solution to meet growing demands in image processing and transmission applications. The code includes configurable parameters for compression ratios, training epochs, and network depth to accommodate different quality requirements.