This study presents a minimalistic but perception-based framework for real-time rendering decisions in virtual reality (VR) applications. The proposed system utilizes a highly compressed visual input representation based on Discrete Cosine Transform (DCT) coefficients and combines it with a tiny neural network to determine whether full scene re-rendering is perceptually necessary. By reframing the rendering task as a binary decision problem, the method enables selective frame reuse, reducing redundant render cycles without sacrificing visual experience. The tiny neural approximator is designed to be extremely compact, ensuring inference times and enabling real-time integration on resource-constrained VR devices. A practical training methodology is proposed that leverages pre-recorded motion sequences and perceptual similarity metrics, such as Structural Similarity (SSIM) and Signal-to-Noise Ratio (SNR), to automatically generate binary training labels without human supervision. Extensive analysis demonstrates the approach’s theoretical feasibility, perceptual relevance, and real-time performance characteristics. The results indicate that perceptually-aware, resource-efficient rendering decisions can be achieved even with minimal model complexity, providing a promising path toward more intelligent and power-conscious VR rendering systems.