Lossy Image Compression

Lossy image compression aims to reduce image file sizes by discarding less important information, balancing data reduction with acceptable image quality degradation. Current research emphasizes developing neural network-based methods, such as autoencoders (including variational and convolutional variants), generative adversarial networks (GANs), and diffusion models, to achieve higher compression ratios while preserving perceptual fidelity and semantic meaning. These advancements are crucial for managing the ever-increasing volume of image data in various applications, from efficient data storage and transmission to improving the performance of downstream tasks like image classification and object detection in resource-constrained environments. Furthermore, research is actively addressing the impact of compression artifacts on various computer vision tasks and exploring methods to mitigate these effects.