用预训练模型的隐空间实现通用图像压缩，不需为不同质量建新模型。
Compact Latent Representation for Image Compression (CLRIC)
- 复用已有模型隐变量，通过可学习函数在隐空间压缩任意质量
- 单次前向计算仅需约25.5个乘加操作/像素，计算量极低
- 兼容多种分辨率和模型，适合资源受限场景
当前图像压缩模型通常需为每个质量等级单独训练，导致训练与存储开销大。为此，我们提出一种创新方法：利用已训练模型（如Stable Diffusion变分自编码器）的隐变量进行感知图像压缩。该方法无需为不同质量级别构建独立模型，通过过拟合的可学习函数对目标模型的隐表示进行任意质量级压缩。这些函数在隐空间运行，确保低计算复杂度，在(1363 × 2048)像素图像上前向传播仅需约25.5 MAC/pixel。该方法在训练与解码阶段均高效利用资源，实现与前沿学习型图像压缩模型相当的感知质量，且具备模型无关与分辨率无关特性，为新型图像压缩方法的发展开辟了新路径。
原文摘要 · Abstract (English)
Current image compression models often require separate models for each quality level, making them resource-intensive in terms of both training and storage. To address these limitations, we propose an innovative approach that utilizes latent variables from pre-existing trained models (such as the Stable Diffusion Variational Autoencoder) for perceptual image compression. Our method eliminates the need for distinct models dedicated to different quality levels. We employ overfitted learnable functions to compress the latent representation from the target model at any desired quality level. These overfitted functions operate in the latent space, ensuring low computational complexity, around $25.5$ MAC/pixel for a forward pass on images with dimensions $(1363 \times 2048)$ pixels. This approach efficiently utilizes resources during both training and decoding. Our method achieves comparable perceptual quality to state-of-the-art learned image compression models while being both model-agnostic and resolution-agnostic. This opens up new possibilities for the development of innovative image compression methods.
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