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An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems [article]

Ankur Mali, Alexander Ororbia, Daniel Kifer, Lee Giles
Recent advances in deep learning have resulted in image compression algorithms that outperform JPEG and JPEG 2000 on the standard Kodak benchmark.  ...  In this paper, we perform the first large-scale comparison of recent state-of-the-art hybrid neural compression algorithms, while exploring the effects of alternative training strategies (when applicable  ...  Conclusions We analyzed the effect of various popular recurrent learning algorithms using iterative refinement on hybrid compression systems for lossy image compression.  ... 
doi:10.48550/arxiv.2201.11782 fatcat:fb7nyk4bjrecxps4q3r76ss4la

Fast Fractal Coding of MRI Images using Deep Reinforcement Learning

Bejoy Varghese, S. Krishnakumar
2021 International Journal of Advanced Computer Science and Applications  
The empirical analysis shows that the proposed algorithm is at least 4 times faster than the competitive methods and the decoding quality is far distinct with a reduction in the bit rate.  ...  The proposed algorithm utilizes Deep Reinforcement Learning (DRL) technique to learn the transformations required to recreate the original image.We avail of the Adaptive Iterated Function System (AIFS)  ...  The empirical analysis shows that the proposed system can be a promising method in the area of medical image compression.  ... 
doi:10.14569/ijacsa.2021.0120492 fatcat:capjgon23vbq7hlri6rkgip5ou

Iris Image Compression Using Deep Convolutional Neural Networks

Ehsaneddin Jalilian, Heinz Hofbauer, Andreas Uhl
2022 Sensors  
deep-learning based lossy image compression technique.  ...  due to the large amounts of data involved, while deep neural networks trained as image auto-encoders have recently emerged a promising direction for advancing the state-of-the-art in image compression,  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22072698 pmid:35408311 pmcid:PMC9002923 fatcat:mzyqjveqlndovi7i3qv734alvy

Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning

Yuhang Dong, W. David Pan, Dongsheng Wu
2019 Entropy  
We provide probabilistic analysis on the impact of misclassification rates on compression performance in terms of the information-theoretic measure of entropy.  ...  This paper addresses a lossless compression of malaria-infected red blood cell images using deep learning.  ...  The neural network in [40] was accelerated by compressing image data with an algorithm based on the discrete cosine transform.  ... 
doi:10.3390/e21111062 fatcat:mizqfdcv5vhy7e5lg537z3fdda

Variational Bayesian Quantization [article]

Yibo Yang, Robert Bamler, Stephan Mandt
2020 arXiv   pre-print
Our experimental results demonstrate the importance of taking into account posterior uncertainties, and show that image compression with the proposed algorithm outperforms JPEG over a wide range of bit  ...  We propose a novel algorithm for quantizing continuous latent representations in trained models.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency  ... 
arXiv:2002.08158v2 fatcat:zplzr5ugsncvplcvixulbmt2dq

Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks

Nick Johnston, Damien Vincent, David Minnen, Michele Covell, Saurabh Singh, Troy Chinen, Sung Jin Hwang, Joel Shor, George Toderici
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM.  ...  Second, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions.  ...  Introduction Previous research showed that deep neural networks can be effectively applied to the problem of lossy image compression [21, 22, 23, 10, 17, 4, 19] .  ... 
doi:10.1109/cvpr.2018.00461 dblp:conf/cvpr/JohnstonVMCSCHS18 fatcat:f5jbserbyfajfgs6v7godaikha

Generative Compression [article]

Shibani Santurkar, David Budden, Nir Shavit
2017 arXiv   pre-print
Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed.  ...  reconstructions at much deeper compression levels for both image and video data.  ...  Discussion All compression algorithms involve a pair of analysis and synthesis transforms that aim to accurately reproduce the original images.  ... 
arXiv:1703.01467v2 fatcat:mpkm5tvw7jdh5iyl3lblxps3ai

Generative Compression

Shibani Santurkar, David Budden, Nir Shavit
2018 2018 Picture Coding Symposium (PCS)  
Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed.  ...  reconstructions at much deeper compression levels for both image and video data.  ...  Discussion All compression algorithms involve a pair of analysis and synthesis transforms that aim to accurately reproduce the original images.  ... 
doi:10.1109/pcs.2018.8456298 dblp:conf/pcs/SanturkarBS18 fatcat:b564wxlorrat3owipbx4422tda

Deep Hierarchical Super-Resolution for Scientific Data Reduction and Visualization [article]

Skylar W. Wurster, Han-Wei Shen, Hanqi Guo, Thomas Peterka, Mukund Raj, Jiayi Xu
2021 arXiv   pre-print
We evaluate application of this algorithm in a data reduction framework by dynamically downscaling input data to an octree-based data structure to represent the multiresolution data before compressing  ...  We train a hierarchy of neural networks, each capable of 2x upscaling in each spatial dimension between two levels of detail, and use these networks in tandem to facilitate large scale factor super resolution  ...  Using neural networks (NNs) for non-error-bounded lossy compression has been studied extensively for image compression with results exceeding JPEG and JPEG2000 [1, 5, 55, [57] [58] [59] .  ... 
arXiv:2107.00462v1 fatcat:37qe5d6v4bgrzb3cy5fnzf2qjm

Neural Multi-scale Image Compression [article]

Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato, Daisuke Okanohara
2018 arXiv   pre-print
This study presents a new lossy image compression method that utilizes the multi-scale features of natural images.  ...  Our proposed model achieves comparable performance to the state-of-the-art model on Kodak and RAISE-1k dataset images, and it encodes a PNG image of size 768 × 512 in 70 ms with a single GPU and a single  ...  Their lossy compression model consists of recurrent neural networkbased encoder and decoder.  ... 
arXiv:1805.06386v1 fatcat:ckcn26gqxbddxb2zm2vsyipesm

Feedback Recurrent AutoEncoder [article]

Yang Yang, Guillaume Sautière, J. Jon Ryu, Taco S Cohen
2020 arXiv   pre-print
In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency.  ...  The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete representation of the data to be learned.  ...  CONCLUSION In this work we presented a new scheme of recurrent autoencoder in the context of lossy online compression of temporally correlated data, and demonstrated its effectiveness on the speech spectrogram  ... 
arXiv:1911.04018v2 fatcat:xpigzrq66bcqbcutt3lwf2jpge

Author Index

2019 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)  
Insertion Technique in Encrypted Image Against Lossy Compression 346 MERGE MODE-BASED DATA EMBEDDING IN SHVC COMPRESSED VIDEO Yoshio Itoh 320 Pre-Inverse Acti ve Noise Control System with Virtual Sensing  ...  learning model with technical analysis for stock price prediction: Empirical study of Semiconductor Company in Ta iwan Tien-Ying Kuo 30 Restoration of Compressed Picture Based on Lightweight Convolutional  ...  Chang-Rong Wu A Continuous Facial Expression Recognition Model based on Deep Learning Method Chao-Ming Wu A Continuous Facial Expression Recognition Model based on Deep Learning Method Zheng-Lin  ... 
doi:10.1109/ispacs48206.2019.8986344 fatcat:tyfkzg6wt5fr3m3ngmh2vdxsea

CocoNet: A deep neural network for mapping pixel coordinates to color values [article]

Paul Andrei Bricman, Radu Tudor Ionescu
2018 arXiv   pre-print
In this paper, we propose a deep neural network approach for mapping the 2D pixel coordinates in an image to the corresponding Red-Green-Blue (RGB) color values.  ...  Our neural image encoding approach has various low-level image processing applications ranging from image encoding, image compression and image denoising to image resampling and image completion.  ...  [1] study the design of deep architectures for lossy image compression.  ... 
arXiv:1805.11357v3 fatcat:jxbaeehionh5zmdgnl6thgdaru

Insights from Generative Modeling for Neural Video Compression [article]

Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt
2021 arXiv   pre-print
While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this work has focused on images  ...  In a similar spirit, we view recently proposed neural video coding algorithms through the lens of deep autoregressive and latent variable modeling.  ...  Considerable progress has been made by applying neural networks to image compression; here we focus on the lossy (instead of lossless) compression setting more relevant to us.  ... 
arXiv:2107.13136v1 fatcat:mdx27avdzbabxayvhpbtbjx74a

Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression [article]

Mu Li, Kede Ma, Jane You, David Zhang, Wangmeng Zuo
2020 arXiv   pre-print
Precise estimation of the probabilistic structure of natural images plays an essential role in image compression.  ...  We demonstrate the promise of CCNs for entropy modeling in both lossless and lossy image compression.  ...  ACKNOWLEDGMENT This work was supported in part by Shenzhen Research Institute of Big Data and Shenzhen Institute of Artificial Intelligence and Robotics for Society.  ... 
arXiv:1906.10057v2 fatcat:spvqxnzksngezlncjjrixskosm
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