Recent advances in learning-based methods have markedly enhanced the capabilities of
image compression. However, these methods struggle with high bit-depth volumetric …

Abstract Autoregressive Initial Bits (ArIB) a framework that combines subimage
autoregression and latent variable models has shown its advantages in lossless image …

Volumetric medical images are extensively employed in medical diagnosis, treatment, and
research, necessitating a significant demand for coding. Currently, JP3D and HEVC are the …

Learned lossless image compression has achieved significant advancements in recent
years. However, existing methods often rely on training amortized generative models on …

Recent advancements in neural compression have surpassed traditional codecs in PSNR
and MS-SSIM measurements. However, at low bit-rates, these methods can introduce …

Medical ultrasound imaging is a widely used non-invasive method for diagnosing diseases.
However, these images contain significant speckle noise, which differs from the …

In medicine, high-quality images are crucial for accurate clinical diagnosis, making lossless
compression essential to preserve image integrity. Neural networks, with their powerful …

In the field of medicine, the rapid advancement of medical technology has significantly
increased the speed of medical image generation, compelling us to seek efficient methods …

The rapid advancements in medical imaging have led to a growing demand for high-
performance lossless compression of large 3D medical image datasets. Unlike natural …

The site conditions that make astronomical observatories in space and on the ground so
desirable—cold and dark—demand a physical remoteness that leads to limited data …