The MC2 lab targets at improving the efficiency of multimedia communication by developing multimedia computing approaches, benefiting from the success of computer vision and machine learning techniques. Specifically, along with the explosion of multimedia content, multimedia communications have become increasingly prominent in communication networks, affecting the daily life of billions of citizens and millions of businesses in the world. The popularity, as a consequence, inceases the amount of data over networks, which is expected to grow almost 40-fold in the next five years. Given the limited spectrum, multimedia applications have encountered the bandwidth-hungry bottleneck. On one hand, high spectral efficiency has been an ongoing request in communication development. On the other hand, pioneering research on delivering the perceived content of human is relieving the bandwidth-hungry issue from the perspective of perceptual compression and coding, in which computer vision and machine learning techniques have been actively studies. This is, however, what the MC2 is focusing on, that is, incorporating the state-of-the-art computer vision and machine learning methodologies into image/video compression and transmission to improve the efficiency of multimedia communications.
