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.