Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

We present a novel type of neural fields that uses general radial bases for signal
representation. State-of-the-art neural fields typically rely on grid-based representations for …

In the field of medical image compression, Implicit Neural Representation (INR) networks
have shown remarkable versatility due to their flexible compression ratios, yet they are …

Efficient storage and sharing of massive biomedical data would open up their wide
accessibility to different institutions and disciplines. However, compressors tailored for …

In this paper, we study how to synthesize a dynamic reference from an external dictionary to
perform conditional coding of the input image in the latent domain and how to learn the …

We propose EVOlutionary Selector (EVOS), an efficient training paradigm for accelerating
Implicit Neural Representation (INR). Unlike conventional INR training that feeds all samples …

The rapid development of large climate models has created the requirement of storing and
transferring massive atmospheric data worldwide. Therefore, data compression is essential …

As a continuous representation function, implicit neural representation has found
widespread applications in various research fields such as computer graphics and computer …

Functional Magnetic Resonance Imaging (fMRI) data is a widely used kind of four-
dimensional biomedical data, which requires effective compression. However, fMRI …

Due to the substantial storage requirements of the 4D medical images, achieving efficient
compression of such images is a crucial topic. Existing traditional image/video coding …