论文标题
卷积自动编码器，用于有损光场压缩
Convolutional Autoencoders for Lossy Light Field Compression
论文作者
论文摘要
在传播信息的熵方面，神经网络宽度的扩展和减少具有众所周知的特性。当小心地堆叠在彼此的顶部时，一个编码器网络和解码器网络会产生一种经常用于压缩的自动编码器。使用此体系结构，我们开发了一种有效的编码和解码4D光场数据的方法，其质量损失最小，具有实质性的压缩因子。我们的最佳结果设法达到了48.6倍的压缩，PSNR为29.46 dB，SSIM为0.8104。编码器和解码器的计算可以实时运行，平均计算时间分别为1.62和1.81，整个网络按照当今的存储标准占据了合理的584MB。
Expansion and reduction of a neural network's width has well known properties in terms of the entropy of the propagating information. When carefully stacked on top of one another, an encoder network and a decoder network produce an autoencoder, often used in compression. Using this architecture, we develop an efficient method of encoding and decoding 4D Light Field data, with a substantial compression factor at a minimal loss in quality. Our best results managed to achieve a compression of 48.6x, with a PSNR of 29.46 dB and a SSIM of 0.8104. Computations of the encoder and decoder can be run in real time, with average computation times of 1.62s and 1.81s respectively, and the entire network occupies a reasonable 584MB by today's storage standards.
