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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.

Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations

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30 March 2020 - 2:55am
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[1] , "Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5053. Accessed: Mar. 30, 2020.
@article{5053-20,
url = {http://sigport.org/5053},
author = { },
publisher = {IEEE SigPort},
title = {Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations},
year = {2020} }
TY - EJOUR
T1 - Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5053
ER -
. (2020). Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations. IEEE SigPort. http://sigport.org/5053
, 2020. Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations. Available at: http://sigport.org/5053.
. (2020). "Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations." Web.
1. . Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5053

Fast CU Size Decision Using Machine Learning for Depth Map Coding in 3D-HEVC


3D-High Efficiency Video Coding (3D-HEVC) is a video compression standard developed for multi-view video plus depth map coding based on the latest HEVC coding standard. We propose an eXtreme Gradient Boosting (XGBoost) system based fast coding unit (CU) level decision for depth maps, which is used to solve the problem of high coding complexity caused by the addition of depth maps and new coding tools in 3D-HEVC. We explore the application of data mining and machine learning in video coding by using texture feature attributes that are highly correlated with CU size.

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Authors:
Ruyi Zhang, Kebin Jia, Pengyu Liu
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29 March 2020 - 10:57pm
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[1] Ruyi Zhang, Kebin Jia, Pengyu Liu, "Fast CU Size Decision Using Machine Learning for Depth Map Coding in 3D-HEVC", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5052. Accessed: Mar. 30, 2020.
@article{5052-20,
url = {http://sigport.org/5052},
author = {Ruyi Zhang; Kebin Jia; Pengyu Liu },
publisher = {IEEE SigPort},
title = {Fast CU Size Decision Using Machine Learning for Depth Map Coding in 3D-HEVC},
year = {2020} }
TY - EJOUR
T1 - Fast CU Size Decision Using Machine Learning for Depth Map Coding in 3D-HEVC
AU - Ruyi Zhang; Kebin Jia; Pengyu Liu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5052
ER -
Ruyi Zhang, Kebin Jia, Pengyu Liu. (2020). Fast CU Size Decision Using Machine Learning for Depth Map Coding in 3D-HEVC. IEEE SigPort. http://sigport.org/5052
Ruyi Zhang, Kebin Jia, Pengyu Liu, 2020. Fast CU Size Decision Using Machine Learning for Depth Map Coding in 3D-HEVC. Available at: http://sigport.org/5052.
Ruyi Zhang, Kebin Jia, Pengyu Liu. (2020). "Fast CU Size Decision Using Machine Learning for Depth Map Coding in 3D-HEVC." Web.
1. Ruyi Zhang, Kebin Jia, Pengyu Liu. Fast CU Size Decision Using Machine Learning for Depth Map Coding in 3D-HEVC [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5052

Fast Depth Intra Coding based on Layer-classification and CNN for 3D-HEVC

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Authors:
Kebin Jia, Pengyu Liu
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29 March 2020 - 10:51pm
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[1] Kebin Jia, Pengyu Liu, "Fast Depth Intra Coding based on Layer-classification and CNN for 3D-HEVC", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5051. Accessed: Mar. 30, 2020.
@article{5051-20,
url = {http://sigport.org/5051},
author = {Kebin Jia; Pengyu Liu },
publisher = {IEEE SigPort},
title = {Fast Depth Intra Coding based on Layer-classification and CNN for 3D-HEVC},
year = {2020} }
TY - EJOUR
T1 - Fast Depth Intra Coding based on Layer-classification and CNN for 3D-HEVC
AU - Kebin Jia; Pengyu Liu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5051
ER -
Kebin Jia, Pengyu Liu. (2020). Fast Depth Intra Coding based on Layer-classification and CNN for 3D-HEVC. IEEE SigPort. http://sigport.org/5051
Kebin Jia, Pengyu Liu, 2020. Fast Depth Intra Coding based on Layer-classification and CNN for 3D-HEVC. Available at: http://sigport.org/5051.
Kebin Jia, Pengyu Liu. (2020). "Fast Depth Intra Coding based on Layer-classification and CNN for 3D-HEVC." Web.
1. Kebin Jia, Pengyu Liu. Fast Depth Intra Coding based on Layer-classification and CNN for 3D-HEVC [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5051

Decode-efficient prefix codes for hierarchical memory models


The cost of uncompressing (decoding) data can be prohibitive in certain real-time applications,
for example when predicting using compressed deep learning models. In many scenarios, it is
acceptable to sacrifice to some extent on compression in the interest of fast decoding. In this
work, we are interested in finding the prefix tree having the best decode time under the constraint
that the code length does not exceed a certain threshold for a natural class of algorithms under

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Authors:
Shashwat Banchhor , Rishikesh R. Gajjala , Yogish Sabharwal , and Sandeep Sen∗
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29 March 2020 - 1:25pm
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[1] Shashwat Banchhor , Rishikesh R. Gajjala , Yogish Sabharwal , and Sandeep Sen∗, "Decode-efficient prefix codes for hierarchical memory models", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5050. Accessed: Mar. 30, 2020.
@article{5050-20,
url = {http://sigport.org/5050},
author = {Shashwat Banchhor ; Rishikesh R. Gajjala ; Yogish Sabharwal ; and Sandeep Sen∗ },
publisher = {IEEE SigPort},
title = {Decode-efficient prefix codes for hierarchical memory models},
year = {2020} }
TY - EJOUR
T1 - Decode-efficient prefix codes for hierarchical memory models
AU - Shashwat Banchhor ; Rishikesh R. Gajjala ; Yogish Sabharwal ; and Sandeep Sen∗
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5050
ER -
Shashwat Banchhor , Rishikesh R. Gajjala , Yogish Sabharwal , and Sandeep Sen∗. (2020). Decode-efficient prefix codes for hierarchical memory models. IEEE SigPort. http://sigport.org/5050
Shashwat Banchhor , Rishikesh R. Gajjala , Yogish Sabharwal , and Sandeep Sen∗, 2020. Decode-efficient prefix codes for hierarchical memory models. Available at: http://sigport.org/5050.
Shashwat Banchhor , Rishikesh R. Gajjala , Yogish Sabharwal , and Sandeep Sen∗. (2020). "Decode-efficient prefix codes for hierarchical memory models." Web.
1. Shashwat Banchhor , Rishikesh R. Gajjala , Yogish Sabharwal , and Sandeep Sen∗. Decode-efficient prefix codes for hierarchical memory models [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5050

c-trie++: A Dynamic Trie Tailored for Fast Prefix Searches


Given a dynamic set K of k strings of total length n whose characters are drawn from an alphabet of size σ, a keyword dictionary is a data structure built on K that provides locate, prefix search, and update operations on K. Under the assumption that α = w / lg σ characters fit into a single machine word w, we propose a keyword dictionary that represents K in n lg σ + O(k lg n) bits of space, supporting all operations in O(m / α + lg α) expected time on an input string of length m in the word RAM model.

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Authors:
Kazuya Tsuruta, Dominik Köppl, Shunsuke Kanda, Yuto Nakashima, Shunsuke Inenaga, Hideo Bannai, Masayuki Takeda
Submitted On:
29 March 2020 - 10:12pm
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[1] Kazuya Tsuruta, Dominik Köppl, Shunsuke Kanda, Yuto Nakashima, Shunsuke Inenaga, Hideo Bannai, Masayuki Takeda, "c-trie++: A Dynamic Trie Tailored for Fast Prefix Searches", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5049. Accessed: Mar. 30, 2020.
@article{5049-20,
url = {http://sigport.org/5049},
author = {Kazuya Tsuruta; Dominik Köppl; Shunsuke Kanda; Yuto Nakashima; Shunsuke Inenaga; Hideo Bannai; Masayuki Takeda },
publisher = {IEEE SigPort},
title = {c-trie++: A Dynamic Trie Tailored for Fast Prefix Searches},
year = {2020} }
TY - EJOUR
T1 - c-trie++: A Dynamic Trie Tailored for Fast Prefix Searches
AU - Kazuya Tsuruta; Dominik Köppl; Shunsuke Kanda; Yuto Nakashima; Shunsuke Inenaga; Hideo Bannai; Masayuki Takeda
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5049
ER -
Kazuya Tsuruta, Dominik Köppl, Shunsuke Kanda, Yuto Nakashima, Shunsuke Inenaga, Hideo Bannai, Masayuki Takeda. (2020). c-trie++: A Dynamic Trie Tailored for Fast Prefix Searches. IEEE SigPort. http://sigport.org/5049
Kazuya Tsuruta, Dominik Köppl, Shunsuke Kanda, Yuto Nakashima, Shunsuke Inenaga, Hideo Bannai, Masayuki Takeda, 2020. c-trie++: A Dynamic Trie Tailored for Fast Prefix Searches. Available at: http://sigport.org/5049.
Kazuya Tsuruta, Dominik Köppl, Shunsuke Kanda, Yuto Nakashima, Shunsuke Inenaga, Hideo Bannai, Masayuki Takeda. (2020). "c-trie++: A Dynamic Trie Tailored for Fast Prefix Searches." Web.
1. Kazuya Tsuruta, Dominik Köppl, Shunsuke Kanda, Yuto Nakashima, Shunsuke Inenaga, Hideo Bannai, Masayuki Takeda. c-trie++: A Dynamic Trie Tailored for Fast Prefix Searches [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5049

Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC

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Authors:
Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu
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29 March 2020 - 9:20am
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[1] Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu, "Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5048. Accessed: Mar. 30, 2020.
@article{5048-20,
url = {http://sigport.org/5048},
author = {Xiaoyu Xu; Jian Qian; Li Yu; Hongkui Wang; Hao Tao; Shengju Yu },
publisher = {IEEE SigPort},
title = {Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC},
year = {2020} }
TY - EJOUR
T1 - Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC
AU - Xiaoyu Xu; Jian Qian; Li Yu; Hongkui Wang; Hao Tao; Shengju Yu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5048
ER -
Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu. (2020). Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC. IEEE SigPort. http://sigport.org/5048
Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu, 2020. Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC. Available at: http://sigport.org/5048.
Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu. (2020). "Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC." Web.
1. Xiaoyu Xu, Jian Qian, Li Yu, Hongkui Wang, Hao Tao, Shengju Yu. Spatial-Temporal Fusion Convolutional Neural Network for Compressed Video enhancement in HEVC [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5048

Encryption Before Compression Coding Scheme for JPEG Image Compression Standard

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Authors:
Kamil Stokfiszewski, Mykhaylo Yatsymirskyy
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29 March 2020 - 6:09am
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[1] Kamil Stokfiszewski, Mykhaylo Yatsymirskyy, "Encryption Before Compression Coding Scheme for JPEG Image Compression Standard", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5047. Accessed: Mar. 30, 2020.
@article{5047-20,
url = {http://sigport.org/5047},
author = {Kamil Stokfiszewski; Mykhaylo Yatsymirskyy },
publisher = {IEEE SigPort},
title = {Encryption Before Compression Coding Scheme for JPEG Image Compression Standard},
year = {2020} }
TY - EJOUR
T1 - Encryption Before Compression Coding Scheme for JPEG Image Compression Standard
AU - Kamil Stokfiszewski; Mykhaylo Yatsymirskyy
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5047
ER -
Kamil Stokfiszewski, Mykhaylo Yatsymirskyy. (2020). Encryption Before Compression Coding Scheme for JPEG Image Compression Standard. IEEE SigPort. http://sigport.org/5047
Kamil Stokfiszewski, Mykhaylo Yatsymirskyy, 2020. Encryption Before Compression Coding Scheme for JPEG Image Compression Standard. Available at: http://sigport.org/5047.
Kamil Stokfiszewski, Mykhaylo Yatsymirskyy. (2020). "Encryption Before Compression Coding Scheme for JPEG Image Compression Standard." Web.
1. Kamil Stokfiszewski, Mykhaylo Yatsymirskyy. Encryption Before Compression Coding Scheme for JPEG Image Compression Standard [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5047

Deep Clustering of Compressed Variational Embeddings


Motivated by the ever-increasing demands for limited communication bandwidth and low-power consumption, we propose a new methodology, named joint Variational Autoencoders with Bernoulli mixture models (VAB), for performing clustering in the compressed data domain. The idea is to reduce the data dimension by Variational Autoencoders (VAEs) and group data representations by Bernoulli mixture models (BMMs).

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Authors:
Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh
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28 March 2020 - 11:30pm
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[1] Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh, "Deep Clustering of Compressed Variational Embeddings", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5046. Accessed: Mar. 30, 2020.
@article{5046-20,
url = {http://sigport.org/5046},
author = {Suya Wu; Enmao Diao; Jie Ding; Vahid Tarokh },
publisher = {IEEE SigPort},
title = {Deep Clustering of Compressed Variational Embeddings},
year = {2020} }
TY - EJOUR
T1 - Deep Clustering of Compressed Variational Embeddings
AU - Suya Wu; Enmao Diao; Jie Ding; Vahid Tarokh
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5046
ER -
Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh. (2020). Deep Clustering of Compressed Variational Embeddings. IEEE SigPort. http://sigport.org/5046
Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh, 2020. Deep Clustering of Compressed Variational Embeddings. Available at: http://sigport.org/5046.
Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh. (2020). "Deep Clustering of Compressed Variational Embeddings." Web.
1. Suya Wu, Enmao Diao, Jie Ding, Vahid Tarokh. Deep Clustering of Compressed Variational Embeddings [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5046

A high efficient cascade coder with predictor blending method for lossless audio compression


In this paper, the improvement of the cascaded prediction method was presented. The prediction method with backward adaptation and extended Ordinary Least Square (OLS+) was presented. An own approach to implementation of the effective context-dependent constant component removal block was used. Also the improved adaptive arithmetic coder with short, medium and long-term adaptation was used and the experiment was carried out comparing the results with other known lossless audio coders against which our method obtained the best efficiency.

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Authors:
Grzegorz Ulacha, Cezary Wernik
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28 March 2020 - 4:45pm
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[1] Grzegorz Ulacha, Cezary Wernik, "A high efficient cascade coder with predictor blending method for lossless audio compression", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5045. Accessed: Mar. 30, 2020.
@article{5045-20,
url = {http://sigport.org/5045},
author = {Grzegorz Ulacha; Cezary Wernik },
publisher = {IEEE SigPort},
title = {A high efficient cascade coder with predictor blending method for lossless audio compression},
year = {2020} }
TY - EJOUR
T1 - A high efficient cascade coder with predictor blending method for lossless audio compression
AU - Grzegorz Ulacha; Cezary Wernik
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5045
ER -
Grzegorz Ulacha, Cezary Wernik. (2020). A high efficient cascade coder with predictor blending method for lossless audio compression. IEEE SigPort. http://sigport.org/5045
Grzegorz Ulacha, Cezary Wernik, 2020. A high efficient cascade coder with predictor blending method for lossless audio compression. Available at: http://sigport.org/5045.
Grzegorz Ulacha, Cezary Wernik. (2020). "A high efficient cascade coder with predictor blending method for lossless audio compression." Web.
1. Grzegorz Ulacha, Cezary Wernik. A high efficient cascade coder with predictor blending method for lossless audio compression [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5045

Image Compression based on Neuroscience Models: Rate-Distortion performance of the Neural Code

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Authors:
Effrosyni Doutsi, Panagiotis Tsakalides
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28 March 2020 - 10:01am
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[1] Effrosyni Doutsi, Panagiotis Tsakalides, "Image Compression based on Neuroscience Models: Rate-Distortion performance of the Neural Code", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5044. Accessed: Mar. 30, 2020.
@article{5044-20,
url = {http://sigport.org/5044},
author = {Effrosyni Doutsi; Panagiotis Tsakalides },
publisher = {IEEE SigPort},
title = {Image Compression based on Neuroscience Models: Rate-Distortion performance of the Neural Code},
year = {2020} }
TY - EJOUR
T1 - Image Compression based on Neuroscience Models: Rate-Distortion performance of the Neural Code
AU - Effrosyni Doutsi; Panagiotis Tsakalides
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5044
ER -
Effrosyni Doutsi, Panagiotis Tsakalides. (2020). Image Compression based on Neuroscience Models: Rate-Distortion performance of the Neural Code. IEEE SigPort. http://sigport.org/5044
Effrosyni Doutsi, Panagiotis Tsakalides, 2020. Image Compression based on Neuroscience Models: Rate-Distortion performance of the Neural Code. Available at: http://sigport.org/5044.
Effrosyni Doutsi, Panagiotis Tsakalides. (2020). "Image Compression based on Neuroscience Models: Rate-Distortion performance of the Neural Code." Web.
1. Effrosyni Doutsi, Panagiotis Tsakalides. Image Compression based on Neuroscience Models: Rate-Distortion performance of the Neural Code [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5044

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