WaveLLM：用于无损图像压缩的小波域大语言模型（LLM）熵建模
WaveLLM: Wavelet-Domain LLM Entropy Modeling for Lossless Image Compression
AI总结：
WaveLLM是一种小波域LLM熵模型，通过小波分解将异构图像预测问题转化为结构化子问题，结合数值接口与任务自适应提示，在三个无损图像压缩基准上优于现有方法。
摘要：
无损图像压缩从根本上依赖于精确的熵建模，这推动了近期向基于大语言模型（LLM）的方法的转变。然而，现有方法主要强调LLM的建模能力，同时直接将图像序列化为像素或残差序列，导致异构信号模式纠缠在单一预测流中。在本文中，我们认为有效的熵建模不仅取决于模型容量，还取决于输入表示的可预测性。基于这一见解，我们提出WaveLLM，一种用于无损图像压缩的小波域LLM熵模型。通过将图像分解为多级小波子带，WaveLLM将异构预测问题转化为一组更结构化、统计上更同质的子问题，这些子问题更适合自回归概率估计。为了在数值编码符号与LLM的原生文本标记空间之间建立桥梁，我们引入了专用数值接口，包含值嵌入和系数预测头。此外，我们设计了任务自适应提示策略，使单一共享模型能够在统一框架内适应不同的子带。WaveLLM基于Llama-3.2-3B构建并采用LoRA适配，在Kodak、http://r0k.us/graphics/kodak/和DIV2K数据集上分别达到2.51、2.24和2.27 bpsp的性能，使用相同小波变换的基于PixelCNN的编解码器在这三个基准上分别优于0.43、0.36和0.46 bpsp，并且在所有三个基准上均超过了近期基于LLM的无损压缩器。
英文摘要：
Lossless image compression fundamentally relies on accurate entropy modeling, driving a recent shift toward large language model (LLM)-based methods. However, existing approaches mainly emphasize the modeling capacity of LLMs while directly serializing images into pixel or residual sequences, leaving heterogeneous signal patterns entangled within a single prediction stream. In this paper, we argue that effective entropy modeling depends not only on model capacity, but also on the predictability of the input representation. Motivated by this insight, we propose WaveLLM, a wavelet-domain LLM-based entropy model for lossless image compression. By decomposing an image into multi-level wavelet subbands, WaveLLM converts a heterogeneous prediction problem into a set of more structured and statistically homogeneous subproblems that are more amenable to autoregressive probability estimation. To bridge numerical coding symbols with the LLM's native text token space, we introduce a dedicated numerical interface featuring value embeddings and a coefficient prediction head. Furthermore, a task-adaptive prompting strategy is designed to enable a single shared model to adapt to diverse subbands within a unified framework. Built on Llama-3.2-3B with LoRA adaptation, WaveLLM achieves 2.51, 2.24, and 2.27 bpsp on Kodak, CLIC Pro, and DIV2K, respectively, outperforming the PixelCNN-based codec using the same wavelet transform by 0.43, 0.36, and 0.46 bpsp, and surpassing recent LLM-based lossless compressors on all three benchmarks.
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