This paper presents a rigorous, quantum-native framework for multi-channel image feature extraction utilizing data-reuploading parameterized quantum circuits (PQCs). While PQCs offer significant potential for machine learning tasks in the noisy-intermediate-scale quantum (NISQ) era, current architectures are predominantly governed by trial-and-error heuristics rather than mathematical principles. To overcome this lack of rigor, we map complex-unitary operations to real three-dimensional rotations, providing a geometric analysis that links Bloch-sphere trajectories directly to PQC expressivity. Building on this theoretical foundation, we propose a novel feature extraction mechanism driven by disjoint multi-pass measurements. Rather than relying on classical convolutional pre-processing, our architecture applies multiple independent measurement passes to individual image tokens. This multi-pass strategy, combined with data-reuploading to capture RGB interdependencies, natively expands the dimensionality of the feature space within a single-qubit framework. Furthermore, to overcome the inherent limits of isolated token processing, we naturally extend our architecture to a multi-qubit setting by strictly applying the analytical design rules established at the single-qubit level. Extensive evaluations on the MNIST, ColorMNIST, and CIFAR datasets confirm that this compact, multi-pass framework achieves the highest reported accuracy among quantum image-processing methods at substantially lower qubit cost. We note that these benchmarks are selected for comparability with existing quantum methods, and that classical vision models remain more accurate on them.