Deep Residual Autoencoder for Blind Universal JPEG Restoration

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Neural-based Compression Scheme for Solar Image Data

Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA's data-intensive imagery missions. We chose NASA's SDO mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of $\sim0.1$ bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO.Comment: Accepted for publication in IEEE Transactions on Aerospace and Electronic Systems (TAES). arXiv admin note: text overlap with arXiv:2210.0647

G-VAE: A Continuously Variable Rate Deep Image Compression Framework

Rate adaption of deep image compression in a single model will become one of the decisive factors competing with the classical image compression codecs. However, until now, there is no perfect solution that neither increases the computation nor affects the compression performance. In this paper, we propose a novel image compression framework G-VAE (Gained Variational Autoencoder), which could achieve continuously variable rate in a single model. Unlike the previous solutions that encode progressively or change the internal unit of the network, G-VAE only adds a pair of gain units at the output of encoder and the input of decoder. It is so concise that G-VAE could be applied to almost all the image compression methods and achieve continuously variable rate with negligible additional parameters and computation. We also propose a new deep image compression framework, which outperforms all the published results on Kodak datasets in PSNR and MS-SSIM metrics. Experimental results show that adding a pair of gain units will not affect the performance of the basic models while endowing them with continuously variable rate

LEARNING-BASED IMAGE COMPRESSION USING MULTIPLE AUTOENCODERS

Advanced video applications in smart environments (e.g., smart cities) bring different challenges associated with increasingly intelligent systems and demanding requirements in emerging fields such as urban surveillance, computer vision in industry, medicine and others. As a consequence, a huge amount of visual data is captured to be analyzed by task-algorithm driven machines. Due to the large amount of data generated, problems may occur at the data management level, and to overcome this problem it is necessary to implement efficient compression methods to reduce the amount of stored resources. This thesis presents the research work on image compression methods using deep learning algorithms analyzing the properties of different algorithms, because recently these have shown good results in image compression. It is also explained the convolutional neural networks and presented a state-of-the-art of autoencoders. Two compression approaches using autoencoders were studied, implemented and tested, namely an object-oriented compression scheme, and algorithms oriented to high resolution images (UHD and 360º images). In the first approach, a video surveillance scenario considering objects such as people, cars, faces, bicycles and motorbikes was regarded, and a compression method using autoencoders was developed with the purpose of the decoded images being delivered for machine vision processing. In this approach the performance was measured analysing the traditional image quality metrics and the accuracy of task driven by machine using decoded images. In the second approach, several high resolution images were considered adapting the method used in the previous approach considering properties of the image, like variance, gradients or PCA of the features, instead of the content that the image represents. Regarding the first approach, in comparison with the Versatile Video Coding (VVC) standard, the proposed approach achieves significantly better coding efficiency, e.g., up to 46.7% BD-rate reduction. The accuracy of the machine vision tasks is also significantly higher when performed over visual objects compressed with the proposed scheme in comparison with the same tasks performed over the same visual objects compressed with the VVC. These results demonstrate that the learningbased approach proposed is a more efficient solution for compression of visual objects than standard encoding. Considering the second approach although it is possible to obtain better results than VVC on the test subsets, the presented approach only presents significant gains considering 360º images