PERFORMANCE ANALYSIS OF ADVANCEMENTS IN VIDEO COMPRESSION WITH DEEP LEARNING

Video content over the internet is increasing day by day with the increasing trends of live video streaming services. People use to capture, share and save their various moments of life using videos. The main challenge before video compression emerged to deal with high quality video content. This led to the emersion of highly efficient and powerful video compression techniques. Videos are disseminated over the internet using efficient and powerful video compression techniques. Existing video compression techniques are designed and optimized manually. Recent researches have shown that deep learning based video compression techniques are giving comparable and better results in comparison to the existing traditional techniques. These results showed the ways to the researchers to work in the direction of applying deep learning concepts in video compression for their practical applicability. This paper gives an insight into the various recent deep learning based video compression techniques and their comparative analysis based on various parameters pertaining to their architectures, compression results, training set, data set, VQMs etc. The comparative and performance analysis presents a future endeavor for scope of further enhancements and optimizations.