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DCC 2020

The Data Compression Conference (DCC) is an international forum for current work on data compression and related applications. Both theoretical and experimental work are of interest. Visit website.

Compressing and Randomly Accessing Sequences


In this paper we consider the problem of storing sequences of symbols in
a compressed format, while supporting random access to the symbols without
decompression. Although this is a well-studied problem when the data is
textual, the kind of sequences we look at are not textual, and we argue
that traditional compression methods used in the text algorithms community
(such as compressors targeting $k$-th order empirical entropy) do not
perform as well on these sequential data, and simpler methods such

Paper Details

Authors:
Laith Ali Abdulsahib, Diego Arroyuelo, Rajeev Raman
Submitted On:
21 April 2020 - 10:31am
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[1] Laith Ali Abdulsahib, Diego Arroyuelo, Rajeev Raman, "Compressing and Randomly Accessing Sequences", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5109. Accessed: Jun. 07, 2020.
@article{5109-20,
url = {http://sigport.org/5109},
author = {Laith Ali Abdulsahib; Diego Arroyuelo; Rajeev Raman },
publisher = {IEEE SigPort},
title = {Compressing and Randomly Accessing Sequences},
year = {2020} }
TY - EJOUR
T1 - Compressing and Randomly Accessing Sequences
AU - Laith Ali Abdulsahib; Diego Arroyuelo; Rajeev Raman
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5109
ER -
Laith Ali Abdulsahib, Diego Arroyuelo, Rajeev Raman. (2020). Compressing and Randomly Accessing Sequences. IEEE SigPort. http://sigport.org/5109
Laith Ali Abdulsahib, Diego Arroyuelo, Rajeev Raman, 2020. Compressing and Randomly Accessing Sequences. Available at: http://sigport.org/5109.
Laith Ali Abdulsahib, Diego Arroyuelo, Rajeev Raman. (2020). "Compressing and Randomly Accessing Sequences." Web.
1. Laith Ali Abdulsahib, Diego Arroyuelo, Rajeev Raman. Compressing and Randomly Accessing Sequences [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5109

Adaptive Stream-based Entropy Coding


Fast data streams are applied by various applications such as multimedia, communication and sensory devices. The amount of data is getting larger and the transfer speed is also getting higher. To address this kind of fast applications, a high performance stream-based data compression mechanism is demanded.

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Authors:
Eisaku Hayakawa, Koichi Marumo
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24 April 2020 - 2:53pm
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[1] Eisaku Hayakawa, Koichi Marumo, "Adaptive Stream-based Entropy Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5107. Accessed: Jun. 07, 2020.
@article{5107-20,
url = {http://sigport.org/5107},
author = {Eisaku Hayakawa; Koichi Marumo },
publisher = {IEEE SigPort},
title = {Adaptive Stream-based Entropy Coding},
year = {2020} }
TY - EJOUR
T1 - Adaptive Stream-based Entropy Coding
AU - Eisaku Hayakawa; Koichi Marumo
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5107
ER -
Eisaku Hayakawa, Koichi Marumo. (2020). Adaptive Stream-based Entropy Coding. IEEE SigPort. http://sigport.org/5107
Eisaku Hayakawa, Koichi Marumo, 2020. Adaptive Stream-based Entropy Coding. Available at: http://sigport.org/5107.
Eisaku Hayakawa, Koichi Marumo. (2020). "Adaptive Stream-based Entropy Coding." Web.
1. Eisaku Hayakawa, Koichi Marumo. Adaptive Stream-based Entropy Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5107

Entropy Coders Based on the Splitting of Lexicographic Intervals

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24 April 2020 - 2:41pm
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[1] , "Entropy Coders Based on the Splitting of Lexicographic Intervals", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5103. Accessed: Jun. 07, 2020.
@article{5103-20,
url = {http://sigport.org/5103},
author = { },
publisher = {IEEE SigPort},
title = {Entropy Coders Based on the Splitting of Lexicographic Intervals},
year = {2020} }
TY - EJOUR
T1 - Entropy Coders Based on the Splitting of Lexicographic Intervals
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5103
ER -
. (2020). Entropy Coders Based on the Splitting of Lexicographic Intervals. IEEE SigPort. http://sigport.org/5103
, 2020. Entropy Coders Based on the Splitting of Lexicographic Intervals. Available at: http://sigport.org/5103.
. (2020). "Entropy Coders Based on the Splitting of Lexicographic Intervals." Web.
1. . Entropy Coders Based on the Splitting of Lexicographic Intervals [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5103

Denoising Deep Boltzmann Machines: Compression for Deep Learning

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24 April 2020 - 12:44pm
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Denoising Deep Botlzmann Machines

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[1] , "Denoising Deep Boltzmann Machines: Compression for Deep Learning", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5097. Accessed: Jun. 07, 2020.
@article{5097-20,
url = {http://sigport.org/5097},
author = { },
publisher = {IEEE SigPort},
title = {Denoising Deep Boltzmann Machines: Compression for Deep Learning},
year = {2020} }
TY - EJOUR
T1 - Denoising Deep Boltzmann Machines: Compression for Deep Learning
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5097
ER -
. (2020). Denoising Deep Boltzmann Machines: Compression for Deep Learning. IEEE SigPort. http://sigport.org/5097
, 2020. Denoising Deep Boltzmann Machines: Compression for Deep Learning. Available at: http://sigport.org/5097.
. (2020). "Denoising Deep Boltzmann Machines: Compression for Deep Learning." Web.
1. . Denoising Deep Boltzmann Machines: Compression for Deep Learning [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5097

Decompressing Lempel-Ziv compressed text


We consider the problem of decompressing the Lempel--Ziv 77 representation of a string $S$ of length $n$ using a working space as close as possible to the size $z$ of the input. The folklore solution for the problem runs in $O(n)$ time but requires random access to the whole decompressed text. Another folklore solution is to convert LZ77 into a grammar of size $O(z\log(n/z))$ and then stream $S$ in linear time. In this paper, we show that $O(n)$ time and $O(z)$ working space can be achieved for constant-size alphabets.

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Authors:
Philip Bille, Mikko Berggren Ettienne, Travis Gagie, Inge Li Gortz, Nicola Prezza
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24 April 2020 - 12:40pm
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DCC '20 slides for Travis Gagie (embedded audio files)

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[1] Philip Bille, Mikko Berggren Ettienne, Travis Gagie, Inge Li Gortz, Nicola Prezza, "Decompressing Lempel-Ziv compressed text", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5090. Accessed: Jun. 07, 2020.
@article{5090-20,
url = {http://sigport.org/5090},
author = {Philip Bille; Mikko Berggren Ettienne; Travis Gagie; Inge Li Gortz; Nicola Prezza },
publisher = {IEEE SigPort},
title = {Decompressing Lempel-Ziv compressed text},
year = {2020} }
TY - EJOUR
T1 - Decompressing Lempel-Ziv compressed text
AU - Philip Bille; Mikko Berggren Ettienne; Travis Gagie; Inge Li Gortz; Nicola Prezza
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5090
ER -
Philip Bille, Mikko Berggren Ettienne, Travis Gagie, Inge Li Gortz, Nicola Prezza. (2020). Decompressing Lempel-Ziv compressed text. IEEE SigPort. http://sigport.org/5090
Philip Bille, Mikko Berggren Ettienne, Travis Gagie, Inge Li Gortz, Nicola Prezza, 2020. Decompressing Lempel-Ziv compressed text. Available at: http://sigport.org/5090.
Philip Bille, Mikko Berggren Ettienne, Travis Gagie, Inge Li Gortz, Nicola Prezza. (2020). "Decompressing Lempel-Ziv compressed text." Web.
1. Philip Bille, Mikko Berggren Ettienne, Travis Gagie, Inge Li Gortz, Nicola Prezza. Decompressing Lempel-Ziv compressed text [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5090

Compact Representation of Graphs with Small Bandwidth and Treedepth


We consider the problem of compact representation of graphs with small bandwidth as well as graphs with small treedepth. These parameters capture structural properties of graphs that come in useful in certain applications.

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Submitted On:
22 April 2020 - 1:50pm
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DCC-2020-Paper-204.pdf

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[1] , "Compact Representation of Graphs with Small Bandwidth and Treedepth", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5089. Accessed: Jun. 07, 2020.
@article{5089-20,
url = {http://sigport.org/5089},
author = { },
publisher = {IEEE SigPort},
title = {Compact Representation of Graphs with Small Bandwidth and Treedepth},
year = {2020} }
TY - EJOUR
T1 - Compact Representation of Graphs with Small Bandwidth and Treedepth
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5089
ER -
. (2020). Compact Representation of Graphs with Small Bandwidth and Treedepth. IEEE SigPort. http://sigport.org/5089
, 2020. Compact Representation of Graphs with Small Bandwidth and Treedepth. Available at: http://sigport.org/5089.
. (2020). "Compact Representation of Graphs with Small Bandwidth and Treedepth." Web.
1. . Compact Representation of Graphs with Small Bandwidth and Treedepth [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5089

Intra Prediction in the Emerging VVC Video Coding

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Authors:
Alexey Filippov, Vasily Rufitskiy, Jianle Chen, Elena Alshina
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31 March 2020 - 7:42pm
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[1] Alexey Filippov, Vasily Rufitskiy, Jianle Chen, Elena Alshina, "Intra Prediction in the Emerging VVC Video Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5088. Accessed: Jun. 07, 2020.
@article{5088-20,
url = {http://sigport.org/5088},
author = {Alexey Filippov; Vasily Rufitskiy; Jianle Chen; Elena Alshina },
publisher = {IEEE SigPort},
title = {Intra Prediction in the Emerging VVC Video Coding},
year = {2020} }
TY - EJOUR
T1 - Intra Prediction in the Emerging VVC Video Coding
AU - Alexey Filippov; Vasily Rufitskiy; Jianle Chen; Elena Alshina
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5088
ER -
Alexey Filippov, Vasily Rufitskiy, Jianle Chen, Elena Alshina. (2020). Intra Prediction in the Emerging VVC Video Coding. IEEE SigPort. http://sigport.org/5088
Alexey Filippov, Vasily Rufitskiy, Jianle Chen, Elena Alshina, 2020. Intra Prediction in the Emerging VVC Video Coding. Available at: http://sigport.org/5088.
Alexey Filippov, Vasily Rufitskiy, Jianle Chen, Elena Alshina. (2020). "Intra Prediction in the Emerging VVC Video Coding." Web.
1. Alexey Filippov, Vasily Rufitskiy, Jianle Chen, Elena Alshina. Intra Prediction in the Emerging VVC Video Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5088

Compressed Quadratization of Higher Order Binary Optimization Problems


Recent hardware advances in quantum and quantum-inspired annealers promise substantial speedup for solving NP-hard combinatorial optimization problems compared to general-purpose computers. These special-purpose hardware are built for solving hard instances of Quadratic Unconstrained Binary Optimization (QUBO) problems. In terms of number of variables and precision of these hardware are usually resource-constrained and they work either in Ising space $\ising$ or in Boolean space $\bool$. Many naturally occurring problem instances are higher-order in nature.

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Authors:
Avradip Mandal, Arnab Roy, Sarvagya Upadhyay, Hayato Ushijima
Submitted On:
31 March 2020 - 4:52pm
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Hobo2QuboDCC-poster.pdf

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[1] Avradip Mandal, Arnab Roy, Sarvagya Upadhyay, Hayato Ushijima, "Compressed Quadratization of Higher Order Binary Optimization Problems", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5087. Accessed: Jun. 07, 2020.
@article{5087-20,
url = {http://sigport.org/5087},
author = {Avradip Mandal; Arnab Roy; Sarvagya Upadhyay; Hayato Ushijima },
publisher = {IEEE SigPort},
title = {Compressed Quadratization of Higher Order Binary Optimization Problems},
year = {2020} }
TY - EJOUR
T1 - Compressed Quadratization of Higher Order Binary Optimization Problems
AU - Avradip Mandal; Arnab Roy; Sarvagya Upadhyay; Hayato Ushijima
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5087
ER -
Avradip Mandal, Arnab Roy, Sarvagya Upadhyay, Hayato Ushijima. (2020). Compressed Quadratization of Higher Order Binary Optimization Problems. IEEE SigPort. http://sigport.org/5087
Avradip Mandal, Arnab Roy, Sarvagya Upadhyay, Hayato Ushijima, 2020. Compressed Quadratization of Higher Order Binary Optimization Problems. Available at: http://sigport.org/5087.
Avradip Mandal, Arnab Roy, Sarvagya Upadhyay, Hayato Ushijima. (2020). "Compressed Quadratization of Higher Order Binary Optimization Problems." Web.
1. Avradip Mandal, Arnab Roy, Sarvagya Upadhyay, Hayato Ushijima. Compressed Quadratization of Higher Order Binary Optimization Problems [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5087

Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication


For lossy image compression, we develop a neural-based system which learns a nonlinear estimator for decoding from quantized representations. The system links two recurrent networks that \help" each other reconstruct same target image patches using complementary portions of spatial context that communicate via gradient signals. This dual agent system builds upon prior work that proposed the iterative refinement algorithm for recurrent neural network (RNN)based decoding which improved image reconstruction compared to standard decoding techniques.

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Authors:
Ankur Mali, Alexander G. Ororbia, C. Lee Giles
Submitted On:
31 March 2020 - 4:39pm
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[1] Ankur Mali, Alexander G. Ororbia, C. Lee Giles , "Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5086. Accessed: Jun. 07, 2020.
@article{5086-20,
url = {http://sigport.org/5086},
author = { Ankur Mali; Alexander G. Ororbia; C. Lee Giles },
publisher = {IEEE SigPort},
title = {Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication},
year = {2020} }
TY - EJOUR
T1 - Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication
AU - Ankur Mali; Alexander G. Ororbia; C. Lee Giles
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5086
ER -
Ankur Mali, Alexander G. Ororbia, C. Lee Giles . (2020). Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication. IEEE SigPort. http://sigport.org/5086
Ankur Mali, Alexander G. Ororbia, C. Lee Giles , 2020. Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication. Available at: http://sigport.org/5086.
Ankur Mali, Alexander G. Ororbia, C. Lee Giles . (2020). "Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication." Web.
1. Ankur Mali, Alexander G. Ororbia, C. Lee Giles . Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5086

Efficient Storage of Images onto DNA Using Vector Quantization


The archiving of digital data is becoming very challenging as conventional electronic devices wear out in time leaving at stake any data that has been stored in them. Therefore, data migration is necessary every 5-10 years. A great percentage of this stored data is "cold", which means that it is very rarely accessed but needs to be safely stored into back-up drives for security and compliance reasons. Unfortunately, the maintenance and replacement of back-up tape drives in big data centers is very expensive both in terms of money and energy.

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Authors:
Melpomeni Dimopoulou, Marc Antonini
Submitted On:
2 April 2020 - 5:05am
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[1] Melpomeni Dimopoulou, Marc Antonini, "Efficient Storage of Images onto DNA Using Vector Quantization", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5085. Accessed: Jun. 07, 2020.
@article{5085-20,
url = {http://sigport.org/5085},
author = {Melpomeni Dimopoulou; Marc Antonini },
publisher = {IEEE SigPort},
title = {Efficient Storage of Images onto DNA Using Vector Quantization},
year = {2020} }
TY - EJOUR
T1 - Efficient Storage of Images onto DNA Using Vector Quantization
AU - Melpomeni Dimopoulou; Marc Antonini
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5085
ER -
Melpomeni Dimopoulou, Marc Antonini. (2020). Efficient Storage of Images onto DNA Using Vector Quantization. IEEE SigPort. http://sigport.org/5085
Melpomeni Dimopoulou, Marc Antonini, 2020. Efficient Storage of Images onto DNA Using Vector Quantization. Available at: http://sigport.org/5085.
Melpomeni Dimopoulou, Marc Antonini. (2020). "Efficient Storage of Images onto DNA Using Vector Quantization." Web.
1. Melpomeni Dimopoulou, Marc Antonini. Efficient Storage of Images onto DNA Using Vector Quantization [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5085

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