  Implicit neural representation (INR) can describe the target scenes with high
fidelity using a small number of parameters, and is emerging as a promising
data compression technique. However, limited spectrum coverage is intrinsic to
INR, and it is non-trivial to remove redundancy in diverse complex data
effectively. Preliminary studies can only exploit either global or local
correlation in the target data and thus of limited performance. In this paper,
we propose a Tree-structured Implicit Neural Compression (TINC) to conduct
compact representation for local regions and extract the shared features of
these local representations in a hierarchical manner. Specifically, we use
Multi-Layer Perceptrons (MLPs) to fit the partitioned local regions, and these
MLPs are organized in tree structure to share parameters according to the
spatial distance. The parameter sharing scheme not only ensures the continuity
between adjacent regions, but also jointly removes the local and non-local
redundancy. Extensive experiments show that TINC improves the compression
fidelity of INR, and has shown impressive compression capabilities over
commercial tools and other deep learning based methods. Besides, the approach
is of high flexibility and can be tailored for different data and parameter
settings. The source code can be found at https://github.com/RichealYoung/TINC .
