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1. Nonlinear Transforms in Learned Image
Compression From a Communication
Perspective
Abstract
Recently, remarkable progress has been made in learned image compression
(LIC), in which nonlinear transforms (NTs) play a crucial role. Although there
are many NT methods for improving the rate distortion performance, all the
existing methods sacrifice the computational complexity and the number of
parameters of the transformation. This paper provides a fundamental novel
viewpoint on nonlinear transforms from a communication perspective, and
shows how this idea can be extended to design efficient NT methods. In
particular, the nonlinear transforms are inferred as signal modulation modules.
Under this extrapolation, the current NTs are generalized as amplitude
modulation that only varies the amplitude of the carrier wave. Therefore, a
nonlinear modulation-like transform (NMLT) which varies the phase angle of
the carrier is proposed. Moreover, this concept is extended by introducing In-
phase/Quadrature (IQ) modulation, which is a boosting technique in

2. communication field, in order to enhance NMLT. Furthermore, the Bit-
interleaved technique in communication is used to guide the optimization of
NTML with IQ. The experimental results on different datasets and backbone
architectures verify the efficiency and robustness of the proposed methods.
For example, when backbone architecture is hyperprior model, our method
achieves 19.37% BD-rate reduction over GDN on the Kodak dataset. In
addition, our method with channel wise autoregressive model leads to the
state-of-the-art rate-distortion performance.