LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
We introduce and validate the lottery codec hypothesis, which states that
untrained subnetworks within randomly initialized networks can serve as
synthesis networks for overfitted image compression, achieving rate-distortion
(RD) performance comparable to trained networks. This hypothesis leads to a new
paradigm for image compression by encoding image statistics into the network
substructure. Building on this hypothesis, we propose LotteryCodec, which
overfits a binary mask to an individual image, leveraging an over-parameterized
and randomly initialized network shared by the encoder and the decoder. To
address over-parameterization challenges and streamline subnetwork search, we
develop a rewind modulation mechanism that improves the RD performance.
LotteryCodec outperforms VTM and sets a new state-of-the-art in single-image
compression. LotteryCodec also enables adaptive decoding complexity through
adjustable mask ratios, offering flexible compression solutions for diverse
device constraints and application requirements.
