2021
DOI: 10.3390/rs13030447
|View full text |Cite
|
Sign up to set email alerts
|

Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression

Abstract: Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform traditional transform coding schemes in terms of rate-distortion trade-off; however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
42
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
9

Relationship

1
8

Authors

Journals

citations
Cited by 36 publications
(42 citation statements)
references
References 20 publications
0
42
0
Order By: Relevance
“…VAE, as a stochastic generative model, has become a popular modeling technique for learning underlying distributions of the input data by reconstruction and producing new data points based on the estimated distribution. Since its first introduction in 2014, the VAE method has been found helpful in numerous applications, such as time-series forecasting [30]- [32], anomaly detection [33]- [35], and image analysis [36].…”
Section: A Data Descriptionmentioning
confidence: 99%
“…VAE, as a stochastic generative model, has become a popular modeling technique for learning underlying distributions of the input data by reconstruction and producing new data points based on the estimated distribution. Since its first introduction in 2014, the VAE method has been found helpful in numerous applications, such as time-series forecasting [30]- [32], anomaly detection [33]- [35], and image analysis [36].…”
Section: A Data Descriptionmentioning
confidence: 99%
“…The paper "Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression" by de Oliveira, V.A., Chabert, M., Oberlin, T., Poulliat, C., Bruno, M., Latry, C., Carlavan, M., Henrot, S., Falzon, F., and Camarero, R. [11] concentrates on design of a complexity-reduced variational autoencoder with attempt to meet the constraints dealing with board satellite compression, time, and memory complexities. A simplified entropy model that preserves the adaptability to the input image is proposed.…”
Section: Overview Of the Issue: Remote Sensing Data Compressionmentioning
confidence: 99%
“…Recently, end-toend CNNs [17], [18] were shown to outperform traditional compression schemes regarding the rate-distortion trade-off, however, at the cost of high computational complexity. Based on the model proposed in [18], we presented in our previous paper [1] a satellite image compression variant with reduced complexity and competitive performance. Furthermore, denoising CNNs can adapt to any non-standard noise statistical model as soon as it can be learned from a representative training data set.…”
Section: Introductionmentioning
confidence: 99%
“…The second proposed approach sequentially uses a first neural architecture for onboard compression and a second one for on ground denoising. For both approaches, the onboard architectures are lightened as much as possible, following the procedure proposed in [1]. The two approaches are shown to outperform the current satellite imaging system and their respective pros and cons are discussed.…”
mentioning
confidence: 99%