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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.

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.

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Authors:
Dominik Köppl, Tomohiro I, Isamu Furuya, Yoshimasa Takabatake, Kensuke Sakai, Keisuke Goto
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26 March 2020 - 4:40am
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[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

Gradient-based Early Termination of CU Partition in VVC Intra Coding

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Authors:
Jing Cui, Tao Zhang, Chenchen Gu, Xinfeng Zhang, Siwei Ma
Submitted On:
26 March 2020 - 3:30am
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[1] Jing Cui, Tao Zhang, Chenchen Gu, Xinfeng Zhang, Siwei Ma, "Gradient-based Early Termination of CU Partition in VVC Intra Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5031. Accessed: Mar. 30, 2020.
@article{5031-20,
url = {http://sigport.org/5031},
author = {Jing Cui; Tao Zhang; Chenchen Gu; Xinfeng Zhang; Siwei Ma },
publisher = {IEEE SigPort},
title = {Gradient-based Early Termination of CU Partition in VVC Intra Coding},
year = {2020} }
TY - EJOUR
T1 - Gradient-based Early Termination of CU Partition in VVC Intra Coding
AU - Jing Cui; Tao Zhang; Chenchen Gu; Xinfeng Zhang; Siwei Ma
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5031
ER -
Jing Cui, Tao Zhang, Chenchen Gu, Xinfeng Zhang, Siwei Ma. (2020). Gradient-based Early Termination of CU Partition in VVC Intra Coding. IEEE SigPort. http://sigport.org/5031
Jing Cui, Tao Zhang, Chenchen Gu, Xinfeng Zhang, Siwei Ma, 2020. Gradient-based Early Termination of CU Partition in VVC Intra Coding. Available at: http://sigport.org/5031.
Jing Cui, Tao Zhang, Chenchen Gu, Xinfeng Zhang, Siwei Ma. (2020). "Gradient-based Early Termination of CU Partition in VVC Intra Coding." Web.
1. Jing Cui, Tao Zhang, Chenchen Gu, Xinfeng Zhang, Siwei Ma. Gradient-based Early Termination of CU Partition in VVC Intra Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5031

Practical Repetition-Aware Grammar Compression


The goal of grammar compression is to construct a small sized context free grammar which uniquely generates the input text data. Among grammar compression methods, RePair is known for its good practical compression performance. MR-RePair was recently proposed as an improvement to RePair for constructing small-sized context free grammar for repetitive text data. However, a compact encoding scheme has not been discussed for MR-RePair. We propose a practical encoding method for MR-RePair and show its effectiveness through comparative experiments.

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25 March 2020 - 8:36pm
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[1] , "Practical Repetition-Aware Grammar Compression", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5030. Accessed: Mar. 30, 2020.
@article{5030-20,
url = {http://sigport.org/5030},
author = { },
publisher = {IEEE SigPort},
title = {Practical Repetition-Aware Grammar Compression},
year = {2020} }
TY - EJOUR
T1 - Practical Repetition-Aware Grammar Compression
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5030
ER -
. (2020). Practical Repetition-Aware Grammar Compression. IEEE SigPort. http://sigport.org/5030
, 2020. Practical Repetition-Aware Grammar Compression. Available at: http://sigport.org/5030.
. (2020). "Practical Repetition-Aware Grammar Compression." Web.
1. . Practical Repetition-Aware Grammar Compression [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5030

Towards Better Compressed Representations


We introduce the problem of computing a parsing where each phrase is of length at most m and which minimizes the zeroth order entropy of parsing. Based on the recent theoretical results we devise a heuristic for this problem. The solution has straightforward application in succinct text representations and gives practical improvements. Moreover the proposed heuristic yields structure which size can be bounded both by |S|H_{m-1}(S) and by |S|/m(H_0(S) + ... + H_{m-1}(S)),where H_{k}(S) is the k-th order empirical entropy of S.

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Authors:
Michał Gańczorz
Submitted On:
25 March 2020 - 1:11pm
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[1] Michał Gańczorz, "Towards Better Compressed Representations", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5029. Accessed: Mar. 30, 2020.
@article{5029-20,
url = {http://sigport.org/5029},
author = {Michał Gańczorz },
publisher = {IEEE SigPort},
title = {Towards Better Compressed Representations},
year = {2020} }
TY - EJOUR
T1 - Towards Better Compressed Representations
AU - Michał Gańczorz
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5029
ER -
Michał Gańczorz. (2020). Towards Better Compressed Representations. IEEE SigPort. http://sigport.org/5029
Michał Gańczorz, 2020. Towards Better Compressed Representations. Available at: http://sigport.org/5029.
Michał Gańczorz. (2020). "Towards Better Compressed Representations." Web.
1. Michał Gańczorz. Towards Better Compressed Representations [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5029

Compressive Classification via Deep Learning using Single-pixel Measurements


Single-pixel camera (SPC) captures encoded projections of the scene in a unique detector such that the number of compressive projections is lower than the size of the image. Traditionally, classification is not performed in the compressive domain because it is necessary to recover the underlying image before to classification. Based on the success of Deep Learning (DL) in classification approaches, this paper proposes to classify images using compressive measurements of SPC.

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Authors:
Jorge Bacca, Nelson Diaz, Henry Arguello
Submitted On:
25 March 2020 - 3:14pm
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[1] Jorge Bacca, Nelson Diaz, Henry Arguello, "Compressive Classification via Deep Learning using Single-pixel Measurements", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5028. Accessed: Mar. 30, 2020.
@article{5028-20,
url = {http://sigport.org/5028},
author = {Jorge Bacca; Nelson Diaz; Henry Arguello },
publisher = {IEEE SigPort},
title = {Compressive Classification via Deep Learning using Single-pixel Measurements},
year = {2020} }
TY - EJOUR
T1 - Compressive Classification via Deep Learning using Single-pixel Measurements
AU - Jorge Bacca; Nelson Diaz; Henry Arguello
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5028
ER -
Jorge Bacca, Nelson Diaz, Henry Arguello. (2020). Compressive Classification via Deep Learning using Single-pixel Measurements. IEEE SigPort. http://sigport.org/5028
Jorge Bacca, Nelson Diaz, Henry Arguello, 2020. Compressive Classification via Deep Learning using Single-pixel Measurements. Available at: http://sigport.org/5028.
Jorge Bacca, Nelson Diaz, Henry Arguello. (2020). "Compressive Classification via Deep Learning using Single-pixel Measurements." Web.
1. Jorge Bacca, Nelson Diaz, Henry Arguello. Compressive Classification via Deep Learning using Single-pixel Measurements [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5028

Video denoising for the hierarchical coding structure in video coding


In modern video codecs, video frames are coded out-of-order following a hierarchical coding structure. The naive uniform video denoising, where denoising is applied indifferently to each frame, does not improve the compression performance. In our work, we only apply denoising to frames at layer 0 and 1. The denoising leads to a significant reduction of bit rates while maintaining temporal correlation. PSNR scores of the filtered frames decrease but PSNR scores of the unfiltered frames remain or even improve.

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Authors:
Jingning Han, Yaowu Xu
Submitted On:
25 March 2020 - 12:36am
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[1] Jingning Han, Yaowu Xu, "Video denoising for the hierarchical coding structure in video coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5027. Accessed: Mar. 30, 2020.
@article{5027-20,
url = {http://sigport.org/5027},
author = {Jingning Han; Yaowu Xu },
publisher = {IEEE SigPort},
title = {Video denoising for the hierarchical coding structure in video coding},
year = {2020} }
TY - EJOUR
T1 - Video denoising for the hierarchical coding structure in video coding
AU - Jingning Han; Yaowu Xu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5027
ER -
Jingning Han, Yaowu Xu. (2020). Video denoising for the hierarchical coding structure in video coding. IEEE SigPort. http://sigport.org/5027
Jingning Han, Yaowu Xu, 2020. Video denoising for the hierarchical coding structure in video coding. Available at: http://sigport.org/5027.
Jingning Han, Yaowu Xu. (2020). "Video denoising for the hierarchical coding structure in video coding." Web.
1. Jingning Han, Yaowu Xu. Video denoising for the hierarchical coding structure in video coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5027

Luma Mapping with Chroma Scaling in Versatile Video Coding


This paper describes a new video coding tool in the Versatile Video Coding standard (VVC) named as luma mapping with chroma scaling (LMCS). Experimental compression performance results for LMCS and non-normative examples for deriving LMCS parameter values are also provided. LMCS has two main components: 1) a process for mapping input luma code values to a new set of code values for use inside the coding loop; and 2) a luma-dependent process for scaling chroma residue values.

Paper Details

Authors:
Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo
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23 March 2020 - 11:59pm
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[1] Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo, "Luma Mapping with Chroma Scaling in Versatile Video Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5026. Accessed: Mar. 30, 2020.
@article{5026-20,
url = {http://sigport.org/5026},
author = {Taoran Lu; Peng Yin; Sean McCarthy; Walt Husak; Tao Chen; Edouard Francois; Christophe Chevance; Franck Hiron; Jie Chen; Ru-Ling Liao; Yan Ye; Jiancong Luo },
publisher = {IEEE SigPort},
title = {Luma Mapping with Chroma Scaling in Versatile Video Coding},
year = {2020} }
TY - EJOUR
T1 - Luma Mapping with Chroma Scaling in Versatile Video Coding
AU - Taoran Lu; Peng Yin; Sean McCarthy; Walt Husak; Tao Chen; Edouard Francois; Christophe Chevance; Franck Hiron; Jie Chen; Ru-Ling Liao; Yan Ye; Jiancong Luo
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5026
ER -
Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo. (2020). Luma Mapping with Chroma Scaling in Versatile Video Coding. IEEE SigPort. http://sigport.org/5026
Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo, 2020. Luma Mapping with Chroma Scaling in Versatile Video Coding. Available at: http://sigport.org/5026.
Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo. (2020). "Luma Mapping with Chroma Scaling in Versatile Video Coding." Web.
1. Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo. Luma Mapping with Chroma Scaling in Versatile Video Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5026

An Adaptive Quantization Based PVC Scheme for HEVC


In order to achieve highly compact representation for videos, we propose an adaptive quantization based perceptual video coding (PVC) scheme in this paper. Because human only perceive the limited discrete-scale quality levels, the perceptual quantization is transformed into the problem of how to determine the maximum quantization parameter (Qp) under the same perceptual quality level. So, the relationship between perceptual quality level and quantization parameter is analyzed with the statistical way in this paper.

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Authors:
Hailang Yang,Hongkui Wang,Li Yu,Junhui Liang,Tiansong Li
Submitted On:
23 March 2020 - 11:11pm
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[1] Hailang Yang,Hongkui Wang,Li Yu,Junhui Liang,Tiansong Li, "An Adaptive Quantization Based PVC Scheme for HEVC", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5025. Accessed: Mar. 30, 2020.
@article{5025-20,
url = {http://sigport.org/5025},
author = {Hailang Yang;Hongkui Wang;Li Yu;Junhui Liang;Tiansong Li },
publisher = {IEEE SigPort},
title = {An Adaptive Quantization Based PVC Scheme for HEVC},
year = {2020} }
TY - EJOUR
T1 - An Adaptive Quantization Based PVC Scheme for HEVC
AU - Hailang Yang;Hongkui Wang;Li Yu;Junhui Liang;Tiansong Li
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5025
ER -
Hailang Yang,Hongkui Wang,Li Yu,Junhui Liang,Tiansong Li. (2020). An Adaptive Quantization Based PVC Scheme for HEVC. IEEE SigPort. http://sigport.org/5025
Hailang Yang,Hongkui Wang,Li Yu,Junhui Liang,Tiansong Li, 2020. An Adaptive Quantization Based PVC Scheme for HEVC. Available at: http://sigport.org/5025.
Hailang Yang,Hongkui Wang,Li Yu,Junhui Liang,Tiansong Li. (2020). "An Adaptive Quantization Based PVC Scheme for HEVC." Web.
1. Hailang Yang,Hongkui Wang,Li Yu,Junhui Liang,Tiansong Li. An Adaptive Quantization Based PVC Scheme for HEVC [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5025

Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks

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23 March 2020 - 4:25pm
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[1] , "Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5024. Accessed: Mar. 30, 2020.
@article{5024-20,
url = {http://sigport.org/5024},
author = { },
publisher = {IEEE SigPort},
title = {Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks},
year = {2020} }
TY - EJOUR
T1 - Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5024
ER -
. (2020). Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks. IEEE SigPort. http://sigport.org/5024
, 2020. Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks. Available at: http://sigport.org/5024.
. (2020). "Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks." Web.
1. . Lossless Multi-Component Image Compression based on Integer Wavelet Coefficient Prediction using Convolutional Neural Networks [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5024

Edge minimization in de Bruijn graphs


This paper introduces the de Bruijn graph edge minimization problem, which is related to the compression of de Bruijn graphs: find the order-k de Bruijn graph with minimum edge count among all orders. We describe an efficient algorithm that solves this problem. Since the edge minimization problem is connected to the BWT compression technique called "tunneling", the paper also describes a way to minimize the length of a tunneled BWT in such a way that useful properties for sequence analysis are preserved.

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Authors:
Thomas Büchler, Enno Ohlebusch, Pascal Weber
Submitted On:
24 March 2020 - 5:56am
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[1] Thomas Büchler, Enno Ohlebusch, Pascal Weber, "Edge minimization in de Bruijn graphs", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5023. Accessed: Mar. 30, 2020.
@article{5023-20,
url = {http://sigport.org/5023},
author = {Thomas Büchler; Enno Ohlebusch; Pascal Weber },
publisher = {IEEE SigPort},
title = {Edge minimization in de Bruijn graphs},
year = {2020} }
TY - EJOUR
T1 - Edge minimization in de Bruijn graphs
AU - Thomas Büchler; Enno Ohlebusch; Pascal Weber
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5023
ER -
Thomas Büchler, Enno Ohlebusch, Pascal Weber. (2020). Edge minimization in de Bruijn graphs. IEEE SigPort. http://sigport.org/5023
Thomas Büchler, Enno Ohlebusch, Pascal Weber, 2020. Edge minimization in de Bruijn graphs. Available at: http://sigport.org/5023.
Thomas Büchler, Enno Ohlebusch, Pascal Weber. (2020). "Edge minimization in de Bruijn graphs." Web.
1. Thomas Büchler, Enno Ohlebusch, Pascal Weber. Edge minimization in de Bruijn graphs [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5023

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