Recent developments in image processing brings the need for solving optimization problems with increasingly large sizes, stretching traditional techniques to their limits. New optimization algorithms need to be designed, paying attention to computational complexity, scalability, and robustness. Majoration-Minimization (MM) approaches have become increasingly popular recently, in both signal/image processing and machine learning areas. The MM framework provides simple, elegant and flexible way to construct optimization algorithms that benefit from solid theoretical foundations and show great practical efficiency. The MajIC project aims at proposing a new generation of MM algorithms that remain efficient in the context of ``big data'' processing, thanks to the integration of parallel, distributed, and online computing strategies.