Compression Artifact

Compression artifacts, distortions introduced by lossy data compression techniques, are a significant concern across various fields, impacting image and video quality and hindering downstream tasks like object recognition and biometric feature extraction. Current research focuses on developing novel algorithms, including those based on singular value decomposition, generative adversarial networks (GANs), and convolutional neural networks (CNNs), to reduce or mitigate these artifacts, often incorporating techniques like adversarial training and implicit neural representations. These advancements are crucial for managing the ever-increasing volume of multimedia data in applications ranging from autonomous driving to high-energy physics, improving both storage efficiency and data usability.