AEON SAPI: Text-Native Visual Communication through Lossless Image Serialization with Adaptive Compression for Universal Compatibility and LLM Integration

We present a framework for text-native visual communication through lossless binary-to-text serialization. By encoding images as Base85 text with adaptive ZLIB compression detection, we enable universal transmission via text-only channels while maintaining pixel-perfect fidelity.

Key contributions: (1) Adaptive compression detection that automatically identifies pre-compressed formats (JPEG, WebP) and bypasses redundant compression, reducing overhead from 33% (Base64) to 25% (Base85); (2) Universal compatibility enabling image transmission through any text channel (JSON APIs, email, chat, SMS) without protocol modification; (3) Experimental demonstration of emergent AI capability where a large language model autonomously recognized and decoded a serialized image format without explicit instruction, reconstructing a 1024×1024 JPEG (366,976 bytes) from 458,720 text characters with 100% byte-perfect fidelity; (4) Transmission latency reduction of 7.6× compared to traditional multipart/form-data protocols.

Furthermore, we demonstrate a critical application for low-bandwidth aerospace environments: by utilizing WebP as the base format within our pipeline, we achieved a 90.3% reduction in payload size (44KB vs 366KB) compared to standard JPEG, enabling high-fidelity satellite imagery transmission via plain text telemetry.

This approach enables novel communication modalities: visual data flows through text-only channels, LLMs process images natively as token sequences, blockchain smart contracts store images as immutable strings, and archival systems achieve format-independent 602-year retention through plain-text durability. Our work challenges the conventional binary/text dichotomy in data transmission and demonstrates that visual communication can be text-native, instantaneous, and universally compatible.

我们提出了一种基于无损二进制转文本序列化的原生文本视觉通信框架。通过将图像编码为带有自适应ZLIB压缩检测的Base85文本，我们能够在保持逐像素保真度的前提下，仅通过文本通道实现通用传输。

核心贡献如下：
1. 自适应压缩检测：可自动识别已预压缩的格式（JPEG、WebP）并跳过冗余压缩，将开销占比从Base64的33%降至Base85的25%；
2. 通用兼容性：无需修改协议即可通过任意文本通道（JSON应用程序接口、电子邮件、聊天软件、短信）传输图像；
3. 涌现式人工智能能力的实验验证：大语言模型（Large Language Model, LLM）无需显式指令即可自主识别并解码序列化图像格式，从458720个文本字符中重建出1024×1024分辨率的JPEG图像（366976字节），且字节级保真度达100%；
4. 传输延迟优化：相较于传统多部分表单数据（multipart/form-data）协议，传输延迟降低7.6倍。

此外，我们还验证了该框架在低带宽航天环境中的关键应用场景：通过在流程中采用WebP作为基础格式，相较于标准JPEG，有效载荷大小降低了90.3%（44KB vs 366KB），可通过纯文本遥测技术实现高保真卫星图像传输。

该框架开创了全新的通信范式：视觉数据可仅通过文本通道流转，大语言模型可将图像原生处理为Token序列，区块链智能合约可将图像存储为不可篡改的字符串，归档系统可依托纯文本的持久特性实现与格式无关的602年长期留存。

本研究打破了数据传输中传统二进制与文本的二元对立范式，证明视觉通信可实现原生文本化、瞬时性与通用兼容性。