Large-Scale Data Embedding in Image Files Using Lexicon-Driven Compression and Modular Metadata Structures

This thesis presents a novel framework for embedding multi-gigabyte datasets into image files using lexicon-driven compression, lossless encoding, and modular parent–child metadata structures. By leveraging PNG containers, the method achieves high-density storage while preserving semantic categories such as memory, emotion, experience, and safety. The approach integrates error correction, signed tables of contents, and lexicon-based symbolic shorthand to ensure both integrity and efficient retrieval. Future extensions highlight the use of optical AI convolution hardware for sustainable, high-speed semantic compression, positioning image-based data embedding as a scalable substrate for cognitive architectures, secure archiving, and steganographic storage.