Sorry, you need to enable JavaScript to visit this website.

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.

Densely Connected Unit based Loop Filter for Short Video Coding


With the rapid development of Internet, short videos draw more and more attentions nowa- days. Due to the small scale of short videos, image-level coding scheme can be applied to improve compression efficiency. In this paper, we propose a densely connected unit based loop filter for short video coding in H.266/VVC. In the proposed loop filter, the densely connected units are specially designed to extract feature maps, and fully decompose videos. By densely connection between layers, the designed units can reuse feature maps, and re- duce the redundancy of features.

Paper Details

Authors:
Shengwei Wang, Peidi Yi, Hongkui Wang, and Li Yu
Submitted On:
28 March 2020 - 8:34am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Session:
Document Year:
Cite

Document Files

Shengwei Wang DCC-2020.pdf

(3)

Subscribe

[1] Shengwei Wang, Peidi Yi, Hongkui Wang, and Li Yu, "Densely Connected Unit based Loop Filter for Short Video Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5042. Accessed: Mar. 30, 2020.
@article{5042-20,
url = {http://sigport.org/5042},
author = {Shengwei Wang; Peidi Yi; Hongkui Wang; and Li Yu },
publisher = {IEEE SigPort},
title = {Densely Connected Unit based Loop Filter for Short Video Coding},
year = {2020} }
TY - EJOUR
T1 - Densely Connected Unit based Loop Filter for Short Video Coding
AU - Shengwei Wang; Peidi Yi; Hongkui Wang; and Li Yu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5042
ER -
Shengwei Wang, Peidi Yi, Hongkui Wang, and Li Yu. (2020). Densely Connected Unit based Loop Filter for Short Video Coding. IEEE SigPort. http://sigport.org/5042
Shengwei Wang, Peidi Yi, Hongkui Wang, and Li Yu, 2020. Densely Connected Unit based Loop Filter for Short Video Coding. Available at: http://sigport.org/5042.
Shengwei Wang, Peidi Yi, Hongkui Wang, and Li Yu. (2020). "Densely Connected Unit based Loop Filter for Short Video Coding." Web.
1. Shengwei Wang, Peidi Yi, Hongkui Wang, and Li Yu. Densely Connected Unit based Loop Filter for Short Video Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5042

Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis


The recent years have witnessed the widespread of light field imaging in interactive and immersive visual applications. To record the directional information of the light rays, larger storage space is required by light field images compared with conventional 2D images. Hence, the efficient compression of light field image is highly desired for further applications. In this paper, we propose a novel light field image compression scheme using multi- branch spatial transformer networks based view synthesis.

Paper Details

Authors:
Jin Wang, Qianwen Wang, Ruiqin Xiong, Qing Zhu, Baocai Yin
Submitted On:
28 March 2020 - 3:09am
Short Link:
Type:
Event:
Presenter's Name:
Session:
Document Year:
Cite

Document Files

DCC 2020 poster.pdf

(1)

Subscribe

[1] Jin Wang, Qianwen Wang, Ruiqin Xiong, Qing Zhu, Baocai Yin, "Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5041. Accessed: Mar. 30, 2020.
@article{5041-20,
url = {http://sigport.org/5041},
author = {Jin Wang; Qianwen Wang; Ruiqin Xiong; Qing Zhu; Baocai Yin },
publisher = {IEEE SigPort},
title = {Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis},
year = {2020} }
TY - EJOUR
T1 - Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis
AU - Jin Wang; Qianwen Wang; Ruiqin Xiong; Qing Zhu; Baocai Yin
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5041
ER -
Jin Wang, Qianwen Wang, Ruiqin Xiong, Qing Zhu, Baocai Yin. (2020). Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis. IEEE SigPort. http://sigport.org/5041
Jin Wang, Qianwen Wang, Ruiqin Xiong, Qing Zhu, Baocai Yin, 2020. Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis. Available at: http://sigport.org/5041.
Jin Wang, Qianwen Wang, Ruiqin Xiong, Qing Zhu, Baocai Yin. (2020). "Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis." Web.
1. Jin Wang, Qianwen Wang, Ruiqin Xiong, Qing Zhu, Baocai Yin. Light Field Image Compression Using Multi-Branch Spatial Transformer Networks Based View Synthesis [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5041

Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC


Rate distortion optimization (RDO) is the basis for algorithm optimization in video coding, such as mode decision, rate control and etc. Minimizing the rate distortion coding cost is usually employed to determine the optimal coding parameters such as quantization level, coding mode, and etc. However, rate and distortion calculations for optimal solution decision from massive possible candidates suffer from dramatically high computation complexity.

Paper Details

Authors:
Xiang Meng, Xiaofeng Huang, Haibin Yin, Shiqi Wang
Submitted On:
28 March 2020 - 2:56am
Short Link:
Type:
Event:
Session:

Document Files

DCC Poster.pdf

(2)

Subscribe

[1] Xiang Meng, Xiaofeng Huang, Haibin Yin, Shiqi Wang, "Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5040. Accessed: Mar. 30, 2020.
@article{5040-20,
url = {http://sigport.org/5040},
author = {Xiang Meng; Xiaofeng Huang; Haibin Yin; Shiqi Wang },
publisher = {IEEE SigPort},
title = {Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC},
year = {2020} }
TY - EJOUR
T1 - Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC
AU - Xiang Meng; Xiaofeng Huang; Haibin Yin; Shiqi Wang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5040
ER -
Xiang Meng, Xiaofeng Huang, Haibin Yin, Shiqi Wang. (2020). Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC. IEEE SigPort. http://sigport.org/5040
Xiang Meng, Xiaofeng Huang, Haibin Yin, Shiqi Wang, 2020. Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC. Available at: http://sigport.org/5040.
Xiang Meng, Xiaofeng Huang, Haibin Yin, Shiqi Wang. (2020). "Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC." Web.
1. Xiang Meng, Xiaofeng Huang, Haibin Yin, Shiqi Wang. Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5040

Reverse Multi-Delimiter Compression Codes


An enhanced version of a recently introduced family of variable length binary codes with multiple pattern delimiters is presented and discussed. These codes are complete, universal, synchronizable, they have monotonic indexing and allow a standard search in compressed files. Comparing the compression rate on natural language texts demonstrates that introduced codes appear to be much superior to other known codes with similar properties. A fast byte-aligned decoding algorithm is constructed, which operates much faster than the one for Fibonacci codes.

Paper Details

Authors:
Anatoly Anisimov
Submitted On:
29 March 2020 - 4:53am
Short Link:
Type:
Event:
Presenter's Name:
Session:
Document Year:
Cite

Document Files

presentation.pptx

(0)

Keywords

Additional Categories

Subscribe

[1] Anatoly Anisimov, "Reverse Multi-Delimiter Compression Codes", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5039. Accessed: Mar. 30, 2020.
@article{5039-20,
url = {http://sigport.org/5039},
author = {Anatoly Anisimov },
publisher = {IEEE SigPort},
title = {Reverse Multi-Delimiter Compression Codes},
year = {2020} }
TY - EJOUR
T1 - Reverse Multi-Delimiter Compression Codes
AU - Anatoly Anisimov
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5039
ER -
Anatoly Anisimov. (2020). Reverse Multi-Delimiter Compression Codes. IEEE SigPort. http://sigport.org/5039
Anatoly Anisimov, 2020. Reverse Multi-Delimiter Compression Codes. Available at: http://sigport.org/5039.
Anatoly Anisimov. (2020). "Reverse Multi-Delimiter Compression Codes." Web.
1. Anatoly Anisimov. Reverse Multi-Delimiter Compression Codes [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5039

On dynamic succinct graph representations

Paper Details

Authors:
Miguel E. Coimbra, Alexandre P. Francisco, Luís M. S. Russo, Guillermo de Bernardo, Susana Ladra, Gonzalo Navarro
Submitted On:
27 March 2020 - 3:05pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Session:
Document Year:
Cite

Document Files

Slides-On_Dynamic_Succinct_Graph_Representations

(6)

Subscribe

[1] Miguel E. Coimbra, Alexandre P. Francisco, Luís M. S. Russo, Guillermo de Bernardo, Susana Ladra, Gonzalo Navarro, " On dynamic succinct graph representations", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5038. Accessed: Mar. 30, 2020.
@article{5038-20,
url = {http://sigport.org/5038},
author = {Miguel E. Coimbra; Alexandre P. Francisco; Luís M. S. Russo; Guillermo de Bernardo; Susana Ladra; Gonzalo Navarro },
publisher = {IEEE SigPort},
title = { On dynamic succinct graph representations},
year = {2020} }
TY - EJOUR
T1 - On dynamic succinct graph representations
AU - Miguel E. Coimbra; Alexandre P. Francisco; Luís M. S. Russo; Guillermo de Bernardo; Susana Ladra; Gonzalo Navarro
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5038
ER -
Miguel E. Coimbra, Alexandre P. Francisco, Luís M. S. Russo, Guillermo de Bernardo, Susana Ladra, Gonzalo Navarro. (2020). On dynamic succinct graph representations. IEEE SigPort. http://sigport.org/5038
Miguel E. Coimbra, Alexandre P. Francisco, Luís M. S. Russo, Guillermo de Bernardo, Susana Ladra, Gonzalo Navarro, 2020. On dynamic succinct graph representations. Available at: http://sigport.org/5038.
Miguel E. Coimbra, Alexandre P. Francisco, Luís M. S. Russo, Guillermo de Bernardo, Susana Ladra, Gonzalo Navarro. (2020). " On dynamic succinct graph representations." Web.
1. Miguel E. Coimbra, Alexandre P. Francisco, Luís M. S. Russo, Guillermo de Bernardo, Susana Ladra, Gonzalo Navarro. On dynamic succinct graph representations [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5038

Temporal Redundancy Reduction in Compressive Video Sensing by using Moving Detection and Inter-Coding


Compressive sensing is a simultaneously signal acquisition and compression technique for efficiently acquiring and reconstructing a signal from a small number of measurements, which can be obtained by linear projections onto sparse signal. In order to further compress the measurements, many works applied intra prediction-based measurement coding. In this paper, we proposed temporal redundancy reduction in compressive video sensing by using moving detection and inter-coding.

Paper Details

Authors:
Jinjia Zhou
Submitted On:
27 March 2020 - 3:13am
Short Link:
Type:
Event:
Presenter's Name:
Session:
Document Year:
Cite

Document Files

DCCPOSTER.pdf

(4)

Subscribe

[1] Jinjia Zhou, "Temporal Redundancy Reduction in Compressive Video Sensing by using Moving Detection and Inter-Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5037. Accessed: Mar. 30, 2020.
@article{5037-20,
url = {http://sigport.org/5037},
author = {Jinjia Zhou },
publisher = {IEEE SigPort},
title = {Temporal Redundancy Reduction in Compressive Video Sensing by using Moving Detection and Inter-Coding},
year = {2020} }
TY - EJOUR
T1 - Temporal Redundancy Reduction in Compressive Video Sensing by using Moving Detection and Inter-Coding
AU - Jinjia Zhou
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5037
ER -
Jinjia Zhou. (2020). Temporal Redundancy Reduction in Compressive Video Sensing by using Moving Detection and Inter-Coding. IEEE SigPort. http://sigport.org/5037
Jinjia Zhou, 2020. Temporal Redundancy Reduction in Compressive Video Sensing by using Moving Detection and Inter-Coding. Available at: http://sigport.org/5037.
Jinjia Zhou. (2020). "Temporal Redundancy Reduction in Compressive Video Sensing by using Moving Detection and Inter-Coding." Web.
1. Jinjia Zhou. Temporal Redundancy Reduction in Compressive Video Sensing by using Moving Detection and Inter-Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5037

DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression


We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately.

Paper Details

Authors:
Jie Ding, Vahid Tarokh
Submitted On:
26 March 2020 - 8:33pm
Short Link:
Type:
Event:
Presenter's Name:
Session:
Document Year:
Cite

Document Files

DRASIC Distributed Recurrent Autoencoder for Scalable Image Compression.pdf

(4)

Subscribe

[1] Jie Ding, Vahid Tarokh, "DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5035. Accessed: Mar. 30, 2020.
@article{5035-20,
url = {http://sigport.org/5035},
author = {Jie Ding; Vahid Tarokh },
publisher = {IEEE SigPort},
title = {DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression},
year = {2020} }
TY - EJOUR
T1 - DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
AU - Jie Ding; Vahid Tarokh
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5035
ER -
Jie Ding, Vahid Tarokh. (2020). DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression. IEEE SigPort. http://sigport.org/5035
Jie Ding, Vahid Tarokh, 2020. DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression. Available at: http://sigport.org/5035.
Jie Ding, Vahid Tarokh. (2020). "DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression." Web.
1. Jie Ding, Vahid Tarokh. DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5035

Spectral Video Compression Using Convolutional Sparse Coding

Paper Details

Authors:
Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello
Submitted On:
26 March 2020 - 11:38am
Short Link:
Type:
Event:
Presenter's Name:
Session:

Document Files

DCCcbarajas2020v2.pdf

(3)

Subscribe

[1] Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello, "Spectral Video Compression Using Convolutional Sparse Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5034. Accessed: Mar. 30, 2020.
@article{5034-20,
url = {http://sigport.org/5034},
author = {Crisostomo Barajas-Solano; Juan-Pablo Ramirez; Henry Arguello },
publisher = {IEEE SigPort},
title = {Spectral Video Compression Using Convolutional Sparse Coding},
year = {2020} }
TY - EJOUR
T1 - Spectral Video Compression Using Convolutional Sparse Coding
AU - Crisostomo Barajas-Solano; Juan-Pablo Ramirez; Henry Arguello
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5034
ER -
Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello. (2020). Spectral Video Compression Using Convolutional Sparse Coding. IEEE SigPort. http://sigport.org/5034
Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello, 2020. Spectral Video Compression Using Convolutional Sparse Coding. Available at: http://sigport.org/5034.
Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello. (2020). "Spectral Video Compression Using Convolutional Sparse Coding." Web.
1. Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello. Spectral Video Compression Using Convolutional Sparse Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5034

Pattern search in grammar-compressed graphs

Paper Details

Authors:
Stefan Böttcher, Rita Hartel, Sven Peeters
Submitted On:
26 March 2020 - 6:50am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Session:

Document Files

DCC20.pdf

(6)

Subscribe

[1] Stefan Böttcher, Rita Hartel, Sven Peeters, "Pattern search in grammar-compressed graphs", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5033. Accessed: Mar. 30, 2020.
@article{5033-20,
url = {http://sigport.org/5033},
author = {Stefan Böttcher; Rita Hartel; Sven Peeters },
publisher = {IEEE SigPort},
title = {Pattern search in grammar-compressed graphs},
year = {2020} }
TY - EJOUR
T1 - Pattern search in grammar-compressed graphs
AU - Stefan Böttcher; Rita Hartel; Sven Peeters
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5033
ER -
Stefan Böttcher, Rita Hartel, Sven Peeters. (2020). Pattern search in grammar-compressed graphs. IEEE SigPort. http://sigport.org/5033
Stefan Böttcher, Rita Hartel, Sven Peeters, 2020. Pattern search in grammar-compressed graphs. Available at: http://sigport.org/5033.
Stefan Böttcher, Rita Hartel, Sven Peeters. (2020). "Pattern search in grammar-compressed graphs." Web.
1. Stefan Böttcher, Rita Hartel, Sven Peeters. Pattern search in grammar-compressed graphs [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5033

Re-Pair in Small Space


Re-Pair is a grammar compression scheme with favorably good compression rates. The computation of Re-Pair comes with the cost of maintaining large frequency tables, which makes it hard to compute Re-Pair on large scale data sets. As a solution for this problem we present, given a text of length n whose characters are drawn from an integer alphabet, an O(n^2) time algorithm computing Re-Pair in n lg max(n, τ) bits of working space including the text space, where τ is the number of terminals and non-terminals.

Paper Details

Authors:
Dominik Köppl, Tomohiro I, Isamu Furuya, Yoshimasa Takabatake, Kensuke Sakai, Keisuke Goto
Submitted On:
26 March 2020 - 4:40am
Short Link:
Type:
Event:
Presenter's Name:
Session:
Document Year:
Cite

Document Files

Poster

(13)

Keywords

Additional Categories

Subscribe

[1] Dominik Köppl, Tomohiro I, Isamu Furuya, Yoshimasa Takabatake, Kensuke Sakai, Keisuke Goto, "Re-Pair in Small Space", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5032. Accessed: Mar. 30, 2020.
@article{5032-20,
url = {http://sigport.org/5032},
author = {Dominik Köppl; Tomohiro I; Isamu Furuya; Yoshimasa Takabatake; Kensuke Sakai; Keisuke Goto },
publisher = {IEEE SigPort},
title = {Re-Pair in Small Space},
year = {2020} }
TY - EJOUR
T1 - Re-Pair in Small Space
AU - Dominik Köppl; Tomohiro I; Isamu Furuya; Yoshimasa Takabatake; Kensuke Sakai; Keisuke Goto
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5032
ER -
Dominik Köppl, Tomohiro I, Isamu Furuya, Yoshimasa Takabatake, Kensuke Sakai, Keisuke Goto. (2020). Re-Pair in Small Space. IEEE SigPort. http://sigport.org/5032
Dominik Köppl, Tomohiro I, Isamu Furuya, Yoshimasa Takabatake, Kensuke Sakai, Keisuke Goto, 2020. Re-Pair in Small Space. Available at: http://sigport.org/5032.
Dominik Köppl, Tomohiro I, Isamu Furuya, Yoshimasa Takabatake, Kensuke Sakai, Keisuke Goto. (2020). "Re-Pair in Small Space." Web.
1. Dominik Köppl, Tomohiro I, Isamu Furuya, Yoshimasa Takabatake, Kensuke Sakai, Keisuke Goto. Re-Pair in Small Space [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5032

Pages