Tingqian Li developed an image encoding optimization for the openvinotoolkit/openvino.genai repository, focusing on the VisionEncoderQwen2VL model. By refactoring preprocessing steps such as resizing, normalization, and reshaping directly into the OpenVINO model graph, Tingqian enabled hardware acceleration and reduced unnecessary data movement. This approach improved image encoding throughput and lowered latency, enhancing end-to-end inference performance for downstream computer vision workflows. The work demonstrated depth in C++ development, model optimization, and integration with OpenVINO. Over the month, Tingqian’s engineering contributed a targeted, technically sound feature that addressed efficiency and performance in model-based image preprocessing pipelines.