深層学習に基づく画像圧縮と品質評価

早大学位記番号:新8427早稲田大

Joint exploration model based light field image coding: A comparative study

© 2017 IEEE. The recent light field imaging technology has been attracting a lot of interests due to its potential applications in a large number of areas including Virtual Reality, Augmented Reality (VR/AR), Teleconferencing, and E-learning. Light Field (LF) data is able to provide rich visual information such as scene rendering with changes in depth of field, viewpoint, and focal length. However, Light Field data usually associates to a critical problem - the massive data. Therefore, compressing LF data is one of the main challenges in LF research. In this context, we present in this paper a comparative study for compressing LF data with not only the widely used image/video coding standards, such as JPEG-2000, H.264/AVC, HEVC and Google/VP9 but also with the most recent image/video coding solution, the Joint Exploration Model. In addition, this paper also proposes a LF image coding flow, which can be used as a benchmark for future LF compression evaluation. Finally, the compression efficiency of these coding solutions is thoroughly compared throughout a rich set of test conditions