LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911136985645056 |
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| author | Wu, Haotian Chen, Gongpu Dragotti, Pier Luigi Gündüz, Deniz |
| author_facet | Wu, Haotian Chen, Gongpu Dragotti, Pier Luigi Gündüz, Deniz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01204 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression Wu, Haotian Chen, Gongpu Dragotti, Pier Luigi Gündüz, Deniz Image and Video Processing Information Theory 68P30, 94A08 I.4.2; E.4 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. |
| title | LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression |
| topic | Image and Video Processing Information Theory 68P30, 94A08 I.4.2; E.4 |
| url | https://arxiv.org/abs/2507.01204 |