SCI: A spectrum concentrated implicit neural compression for biomedical
data
- URL: http://arxiv.org/abs/2209.15180v1
- Date: Fri, 30 Sep 2022 02:05:39 GMT
- Title: SCI: A spectrum concentrated implicit neural compression for biomedical
data
- Authors: Runzhao Yang, Tingxiong Xiao, Yuxiao Cheng, Qianni Cao, Jinyuan Qu,
Jinli Suo, Qionghai Dai
- Abstract summary: We propose an adaptive compression approach SCI, which adaptively partitions the target data into blocks matching the concentrated spectrum envelop of the adopted INR.
Experiments show SCI's superior performance over conventional techniques and wide applicability across diverse medical data.
- Score: 26.621981063249645
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Massive collection and explosive growth of the huge amount of medical data,
demands effective compression for efficient storage, transmission and sharing.
Readily available visual data compression techniques have been studied
extensively but tailored for nature images/videos, and thus show limited
performance on medical data which are of different characteristics. 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 medical 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
compression-oriented INR architecture. Further, we design a funnel shaped
neural network capable of covering broad spectrum of complex medical data and
achieving high compression ratio. Based on this design, we conduct compression
via optimization under given budget and propose an adaptive compression
approach SCI, which adaptively partitions the target data into blocks matching
the concentrated spectrum envelop of the adopted INR, and allocates parameter
with high representation accuracy under given compression ratio. The
experiments show SCI's superior performance over conventional techniques and
wide applicability across diverse medical data.
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