This repository contains the implementation and experiments for a project on parallel RGB-to-grayscale image conversion using Python. This project presents a Parallel Image Grayscale Conversion approach, which uses data parallelism to speed up the grayscale converting process. The objective is to emphasize the function of parallelization in contemporary image processing systems and show how it dramatically boosts performance when compared to sequential execution
- Sequential implementation of grayscale conversion
- Parallel implementation with race condition
- Parallel implementation with synchronization (Lock) to ensure correctness
Execution time measurement and performance evaluation are integrated directly within the code using Python’s time.perf_counter(). Different image sizes and parallel configurations are tested to study performance and scalability.
This repository is provided as a reference for learners interested in parallel programming, race conditions, and synchronization techniques.