Neural Network Based Image Compression

Neural network-based image compression aims to leverage the power of deep learning to achieve superior compression ratios and visual quality compared to traditional methods. Current research focuses on developing versatile models capable of handling variable bit rates and spatial scalability, often employing transformer architectures, generative adversarial networks (GANs), or other deep learning approaches to optimize rate-distortion trade-offs and enhance perceptual realism. These advancements are significant for various applications, including space exploration (handling large datasets from missions like the Solar Dynamics Observatory) and multimedia processing, where efficient and high-quality compression is crucial. The ongoing standardization efforts, such as with JPEG-AI, highlight the growing importance and practical impact of this technology.