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Significant advances in video compression system have been made in the pastseveral decades to satisfy the nearly exponential growth of Internet-scalevideo traffic. From the application perspective, we have identified three majorfunctional blocks including pre-processing, coding, and post-processing, thathave been continuously investigated to maximize the end-user quality ofexperience (QoE) under a limited bit rate budget. Recently, artificialintelligence (AI) powered techniques have shown great potential to furtherincrease the efficiency of the aforementioned functional blocks, bothindividually and jointly. In this article, we review extensively recenttechnical advances in video compression system, with an emphasis on deep neuralnetwork (DNN)-based approaches; and then present three comprehensive casestudies. On pre-processing, we show a switchable texture-based video codingexample that leverages DNN-based scene understanding to extract semantic areasfor the improvement of subsequent video coder. On coding, we present anend-to-end neural video coding framework that takes advantage of the stackedDNNs to efficiently and compactly code input raw videos via fully data-drivenlearning. On post-processing, we demonstrate two neural adaptive filters torespectively facilitate the in-loop and post filtering for the enhancement ofcompressed frames. Finally, a companion website hosting the contents developedin this work can be accessed publicly athttps://purdueviper.github.io/dnn-coding/.

Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies