The Impact of Binary Serialization on Lottie Files: A Comparative Study

Abstract

As interactive web animations become more essential to modern user interface, the demand for more scale-able animation formats increases. the standard Lottie format relies on human-readable JSON metadata for asset delivery. However, text-based serialization imposes a severe computational parsing tax on client devices, creating performance bottlenecks. This study addresses the lack of empirical data regarding a specialized, schema-driven binary serialization format tailored specifically to enhance the transfer and loading of Lottie animations. This study developed an alternative binary format prototype, TINY-LOTTIE, which structures data layout to align directly with modern machine architectures. Benchmarking across a structurally diverse dataset (N = 9) reveals that it was able to achieve 7.46 X size reduction compared to its original JSON alternative. Moreover, this was accomplished while keeping the encoding time under 45ms range. The average encoding time reduction is about 80.3X compared to Dot-Lottie. These findings demonstrate that shifting the optimization paradigm from general stream compression to structural binary formatting unlocks deterministic scalability and enables low-level CPU parallelization. This research palliates a critical gap between network transmission budgets and client-side computational latency, establishing a foundational framework for next-generation, high-performance multimedia assets.