OBJECTIVE IMAGE QUALITY ANALYSIS OF CONVOLUTIONAL NEURAL NETWORK LIGHT FIELD CODING

被引：0
|
作者：
Medda, Daniele [1 ]
Song, Wei [2 ]
Perra, Cristian [1 ]
机构：
[1] Univ Cagliari, UdR CNIT, DIEE, Cagliari, Italy
[2] Shanghai Ocean Univ, Coll Informat Technol, Shanghai, Peoples R China
来源：
2019 8TH EUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING (EUVIP 2019) | 2019年
基金：
中国国家自然科学基金;
关键词：
convolutional neural network; autoencoder; light field; coding; compression; image quality;
D O I：
10.1109/euvip47703.2019.8946230
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Light field digital images are novel image modalities for capturing a sampled representation of the plenoptic function. A large amount of data is typically associated to a single sample of a scene, and data compression tools are required in order to develop systems and applications for light field communications. This paper presents the study of the performance of a convolutional neural network autoencoder as a tool for digital light field image compression. Testing conditions and a framework for the experimental evaluation are proposed for this study. Different encoders and coding conditions are taken into consideration, obtained results are reported and critically discussed.
引用
收藏
页码：163 / 168
页数：6
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