Hybrid Representation for 4D Medical Image Compression

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 methods have achieved remarkable results in most compression tasks, but their performance in encoding 4D medical images remain poor. This is because these methods cannot fully exploit the spatio-temporal correlations in 4D images. Recently, implicit neural representation (INR) based image/video compression methods have made significant progress, with coding performance comparable to traditional methods. However, they also suffer from significant performance losses in 4D medical image compression like traditional methods. In this paper, we propose an efficient hybrid representation framework, which includes six learnable feature planes and a tiny MLP decoder. This framework alleviates the issue of previous methods lacking the ability to utilize the spatio-temporal correlations in 4D medical images, enabling it to capture these information more effectively. We also introduce a novel adaptive plane scaling strategy that allocates the numbers of parameter in each plane based on the resolution of the image. This design allows the model to further enhance the reconstruction quality at the same compression ratio. Extensive experiments show that our model achieves better RD performance compared to traditional and INR-based methods, and it also offers faster encoding speeds than INR-based methods.