Modern compression theory is based on Shannon’s foundational insight that information is governed by entropy. Golomb coding, devised in the 1960’s, is a remarkably efficient solution for encoding geometrically distributed integers, and remains in widespread use because of its simplicity and effectiveness. In this paper, I revisit Golomb coding not merely as a mathematical transformation, but as a process of dimensional projection.I propose a generalized compression framework derived from the principles of dimensional projection. The process takes structured data and temporarily lifts it into higher-dimensional space to expose latent informational geometry, then systematically re-flattens it into a minimal entropy representation. Specifically, I reinterpret Golomb coding as a 1D-to-2D projection followed by structured reduction, and generalize this concept into a proposed 4D projection-based model applicable across multiple data types—text, DNA, images, audio, video, and more.The proposed framework provides a unifying geometric perspective on compression, revealing new axes of redundancy not captured by traditional frequency-based methods. This theoretical work offers a foundation for future research in entropy minimization, structural compression, and the dimensional nature of information itself.