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Autoencoding Implicit Neural Representations for Image Compression
Tuan Pham · Yibo Yang · Stephan Mandt
Event URL: https://openreview.net/forum?id=bZn0XOm37w »

Implicit Neural Representations (INRs) are increasingly popular methods for representing a variety of signals (Sitzmann et al., 2020b; Park et al., 2019; Mildenhall et al., 2021). Given their advantages over traditional signal representations, there are strong incentives to leverage them for signal compression. Here we focus on image compression, where recent INR-based approaches learn a base INR network shared across images, and infer/quantize a latent representation for each image in a second stage (Dupont et al., 2022; Schwarz &Teh, 2022; Schwarz et al., 2023). In this work, we view these approaches as special cases of nonlinear transform coding (NTC), and instead propose an end-to-end approach directly optimized for rate-distortion (R-D) performance. We essentially perform NTC with an INR-based decoder, achieving significantly faster training and improved R-D performance, although still falling short of that of state-of-the-art NTC approaches. By viewing an INR base network as a convolutional decoder with 1x1 convolutions, we can also better understand its inferior R-D performance through this inherent architectural constraint.

Author Information

Tuan Pham
Yibo Yang (University of California, Irivine)
Stephan Mandt (University of California, Irivine)

Stephan Mandt is an Assistant Professor of Computer Science at the University of California, Irvine. From 2016 until 2018, he was a Senior Researcher and head of the statistical machine learning group at Disney Research, first in Pittsburgh and later in Los Angeles. He held previous postdoctoral positions at Columbia University and at Princeton University. Stephan holds a PhD in Theoretical Physics from the University of Cologne. He is a Fellow of the German National Merit Foundation, a Kavli Fellow of the U.S. National Academy of Sciences, and was a visiting researcher at Google Brain. Stephan serves regularly as an Area Chair for NeurIPS, ICML, AAAI, and ICLR, and is a member of the Editorial Board of JMLR. His research is currently supported by NSF, DARPA, IBM, and Qualcomm.

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