为AI生成内容设计新文件格式，压缩比达1/10000
Towards Defining an Efficient and Expandable File Format for AI-Generated Contents
- 通过压缩生成语法而非像素数据实现超低码率
- 三因素组合使压缩比最高达1/10000且保持高保真
- 支持未来模型扩展，适合内容创作者与平台开发者
近期，AI生成内容（AIGC）因其强大的创作能力受到广泛关注。然而，大量高质量AIGC图像的存储与传输对现有文件格式带来新挑战。为此，我们定义了一种针对AIGC图像的新文件格式AIGIF，实现超低码率编码。不同于传统文件格式对像素空间的直观压缩，AIGIF转而压缩生成语法。这引发关键问题：哪些生成语法元素（如文本提示、设备配置等）对压缩/传输至关重要？我们系统研究了三大核心因素的影响：平台、生成模型和数据配置。实验发现，合理设计的可组合比特流结构在三者协同下，压缩比最高可达1/10,000，同时仍保证高保真度。此外，AIGIF引入可扩展语法，支持未来最先进生成模型的接入。
原文摘要 · Abstract (English)
Recently, AI-generated content (AIGC) has gained significant traction due to its powerful creation capability. However, the storage and transmission of large amounts of high-quality AIGC images inevitably pose new challenges for recent file formats. To overcome this, we define a new file format for AIGC images, named AIGIF, enabling ultra-low bitrate coding of AIGC images. Unlike compressing AIGC images intuitively with pixel-wise space as existing file formats, AIGIF instead compresses the generation syntax. This raises a crucial question: Which generation syntax elements, e.g., text prompt, device configuration, etc, are necessary for compression/transmission? To answer this question, we systematically investigate the effects of three essential factors: platform, generative model, and data configuration. We experimentally find that a well-designed composable bitstream structure incorporating the above three factors can achieve an impressive compression ratio of even up to 1/10,000 while still ensuring high fidelity. We also introduce an expandable syntax in AIGIF to support the extension of the most advanced generation models to be developed in the future.
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