This dissertation demonstrates the mutual beneficence of non-realistic computer graphics and perception with two rendering frameworks and accompanying psychophysical studies: (1) Inspired by low-level human perception, a novel image-based abstraction framework simplifies and enhances images to make them easier to understand and remember. (2) A non-realistic rendering framework generates isolated visual shape cues to study human perception of fast-moving objects. The first framework leverages perception to increase effectiveness of (non-realistic) images for visually-driven tasks, while the second framework uses non-realistic images to learn about task-specific perception, thus closing the loop. As instances of the bi-directional connections between perception and non-realistic imagery, the frameworks illustrate numerous benefits including effectiveness (e.g. better recognition of abstractions versus photographs), high performance (e.g. real-time image abstraction), and relevance (e.g. shape perception in non-impoverished conditions).