Optimal Quantization on Spherical Surfaces: Continuous and Discrete Models - A Beginner-Friendly Expository Study (2511.05099v1)

Abstract: This expository paper provides a unified and pedagogical introduction to optimal quantization for probability measures supported on spherical surfaces, emphasizing both continuous and discrete settings. We first present a detailed geometric and analytical foundation of quantization on the unit sphere, including definitions of great and small circles, spherical triangles, geodesic distance, Slerp interpolation, the Fr\'echet mean, spherical Voronoi regions, centroid conditions, and quantization dimensions. Building upon this framework, we develop explicit continuous and discrete quantization models on spherical curves -great circles, small circles, and great circular arcs -supported by rigorous derivations and pedagogical exposition. For uniform continuous distributions, we compute optimal sets of $n$-means and the associated quantization errors on these curves; for discrete distributions, we analyze antipodal, equatorial, tetrahedral, and finite uniform configurations, illustrating convergence to the continuous model. The central conclusion is that for a uniform probability distribution supported on a one-dimensional geodesic subset of total length $L$, the optimal $n$-means form a uniform partition and the quantization error satisfies $V_n = L^2/(12n^2)$. The exposition emphasizes geometric intuition, detailed derivations, and clear step-by-step reasoning, making it accessible to beginning graduate students and researchers entering the study of quantization on manifolds.