Pixelated: A Lossless Data Transformation Framework Built on PNG Compression

The rapid growth of digital data has created an urgent need for innovative storage solutions. Transactional data, with its diverse formats and row-based structure, is a significant contributor to this challenge. Existing storage methods struggle to compress heterogeneous structured data efficiently due to limited redundancy exploitation across mixed data types. This study introduces Pixelated, a novel lossless data transformation and storage framework that converts structured transactional data into pixel representations stored in Portable Network Graphics (PNG) format. Pixelated introduces a new data representation strategy that enables the existing DEFLATE compression mechanism within PNG to exploit patterns and redundancy in heterogeneous transactional datasets more effectively. Designed for datasets containing numerical, categorical, and datetime values, Pixelated achieves average compression rates exceeding 90%. The framework is evaluated across benchmark and real-world datasets, demonstrating competitive performance against ZIP, Apache Parquet, and Python Pickle. Detailed methodology, redundancy characteristics, limitations, and performance results are presented.