  Massive collection and explosive growth of biomedical data, demands effective
compression for efficient storage, transmission and sharing. Readily available
visual data compression techniques have been studied extensively but tailored
for natural images/videos, and thus show limited performance on biomedical data
which are of different features and larger diversity. Emerging implicit neural
representation (INR) is gaining momentum and demonstrates high promise for
fitting diverse visual data in target-data-specific manner, but a general
compression scheme covering diverse biomedical data is so far absent. To
address this issue, we firstly derive a mathematical explanation for INR's
spectrum concentration property and an analytical insight on the design of INR
based compressor. Further, we propose a Spectrum Concentrated Implicit neural
compression (SCI) which adaptively partitions the complex biomedical data into
blocks matching INR's concentrated spectrum envelop, and design a funnel shaped
neural network capable of representing each block with a small number of
parameters. Based on this design, we conduct compression via optimization under
given budget and allocate the available parameters with high representation
accuracy. The experiments show SCI's superior performance to state-of-the-art
methods including commercial compressors, data-driven ones, and INR based
counterparts on diverse biomedical data. The source code can be found at
https://github.com/RichealYoung/ImplicitNeuralCompression.git.
