随机网络中隐藏的子网络可高效压缩单张图像，性能媲美训练模型。
LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression
- 从随机初始化网络中搜索隐含子结构，作为图像合成器。
- 单图压缩性能超越VTM，达新基准，比特率更低、失真更小。
- 支持灵活解码复杂度调节，适配不同设备需求。
我们提出并验证了彩票编码器假设：在随机初始化的网络中，未经训练的子网络可作为过拟合图像压缩的合成网络，其率失真（RD）性能与训练网络相当。该假设催生了一种新范式，即通过网络子结构编码图像统计信息。基于此，我们提出LotteryCodec，对单张图像过拟合一个二值掩码，利用共享的过参数化随机初始化网络作为编码器和解码器。为应对过参数化挑战并简化子网络搜索，我们设计了回溯调制机制，提升率失真性能。LotteryCodec优于VTM，在单图像压缩上达到新基准。同时，通过可调掩码比例实现自适应解码复杂度，为多样设备约束与应用需求提供灵活压缩方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。