End-to-End Learned Image Compression with Augmented Normalizing Flows

Ho, Yung-Han; Chan, Chih-Chun; Peng, Wen-Hsiao; Hang, Hsueh-Ming

doi:10.1109/cvprw53098.2021.00220

“…Experimental results demonstrate that the enhanced INN approach outperforms state-of-the-art learned image compression methods and VVC compression standards, especially on high-resolution image datasets. Furthermore, Ho et al [105,106] attempted to use Augmented Normalizing Flows (ANFs) [107] for lossy image compression, termed ANFIC. ANF is a specific type of normalizing flow model that augments the input with independent noise, enabling a smoother transformation from the augmented input to the latent space.…”

Section: Miscellaneous Topicsmentioning

confidence: 99%

Unveiling the Future of Human and Machine Coding: A Survey of End-to-End Learned Image Compression

Huang,

Wu

2024

Entropy

0

View full text Add to dashboard Cite

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases.

show abstract

“…Experimental results demonstrate that the enhanced INN approach outperforms state-of-the-art learned image compression methods and VVC compression standards, especially on high-resolution image datasets. Furthermore, Ho et al [105,106] attempt to use Augmented Normalizing Flows (ANF) [107] for lossy image compression, termed ANFIC. ANF is a specific type of normalizing flow model that augments the input with independent noise, enabling a smoother transformation from the augmented input to the latent space.…”

Section: Miscellaneous Topicsmentioning

confidence: 99%

Unveiling the Future of Human and Machine Coding: A Survey of End-to-End Learned Image Compression

Huang,

Wu

2024

Preprint

0

View full text Add to dashboard Cite

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases.

show abstract