Learned Image

Learned image compression leverages deep learning to achieve superior compression ratios compared to traditional methods, aiming to minimize both distortion and bitrate while enhancing perceptual quality. Current research focuses on improving efficiency through architectural innovations like multi-scale networks and color space optimization, as well as addressing limitations such as handling diverse image types (e.g., fingerprints, screen content) and improving real-time performance via optimized hardware implementations. These advancements have significant implications for various applications, including efficient storage of biometric data, bandwidth-constrained streaming, and enabling new possibilities in image processing tasks directly on compressed representations.