Writing high-performance image processing code is challenging and labor-intensive. Automatic compiler optimization is appealing, and has been the subject of much research, but human programmers are still often able to optimize their code in ways that significantly outperform automatic compilers. As a result, high-performance image processing code still relies on painstaking hand optimization. The Halide programming language simplifies this task by decoupling high-level algorithms from “schedules” which define how to optimize those computations, including choices that control parallelism and cache locality. However, even with this abstraction, it is still challenging for Halide programmers to understand complicated scheduling strategies and productively write valid, optimized schedules. To address this, we propose a programming support method called “guided optimization.” Guided optimization provides programmers a set of valid optimization options and interactive feedback about their current choices, which enables them to comprehend and efficiently optimize the image processing code without the time-consuming process of trial-and-error in traditional text editors. We implemented a proof-of-concept system, Roly-poly, which integrates guided optimization, program visualization, and scheduling cost estimation to support the comprehension and development of efficient Halide image processing codes. We conducted a user study with novice Halide programmers and confirmed that Roly-poly and its guided optimization was informative, increased productivity, and resulted in higher-performing schedules in less time.