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

页码：163 / 168

页数：6

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