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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.

Weighted Adaptive Huffman Coding


Huffman coding is known to be optimal in case the alphabet is known in advance, the set of codewords is fixed and each codeword consists of an integral number of bits. If one of these conditions is violated, optimality is not guaranteed.
In the {\it dynamic\/} variant of Huffman coding the encoder and decoder maintain identical copies of the model; at each position, the model consists of the frequencies of the elements processed so far.

Paper Details

Authors:
Aharon Fruchtman, Yoav Gross, Shmuel T. Klein, Dana Shapira
Submitted On:
23 March 2020 - 11:05am
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[1] Aharon Fruchtman, Yoav Gross, Shmuel T. Klein, Dana Shapira, "Weighted Adaptive Huffman Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5022. Accessed: Mar. 30, 2020.
@article{5022-20,
url = {http://sigport.org/5022},
author = {Aharon Fruchtman; Yoav Gross; Shmuel T. Klein; Dana Shapira },
publisher = {IEEE SigPort},
title = {Weighted Adaptive Huffman Coding},
year = {2020} }
TY - EJOUR
T1 - Weighted Adaptive Huffman Coding
AU - Aharon Fruchtman; Yoav Gross; Shmuel T. Klein; Dana Shapira
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5022
ER -
Aharon Fruchtman, Yoav Gross, Shmuel T. Klein, Dana Shapira. (2020). Weighted Adaptive Huffman Coding. IEEE SigPort. http://sigport.org/5022
Aharon Fruchtman, Yoav Gross, Shmuel T. Klein, Dana Shapira, 2020. Weighted Adaptive Huffman Coding. Available at: http://sigport.org/5022.
Aharon Fruchtman, Yoav Gross, Shmuel T. Klein, Dana Shapira. (2020). "Weighted Adaptive Huffman Coding." Web.
1. Aharon Fruchtman, Yoav Gross, Shmuel T. Klein, Dana Shapira. Weighted Adaptive Huffman Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5022

The Exponential Distribution in Rate Distortion Theory: The Case of Compression with Independent Encodings


In this paper, we consider the rate-distortion problem where a source X is encoded into k parallel descriptions Y1, . . . , Yk, such that the error signals X - Yi, i = 1, . . . , k, are mutually independent given X. We show that if X is one-sided exponentially distributed, the optimal decoder (estimator) under the one-sided absolute error criterion, is simply given by the maximum of the outputs Y1, . . . , Yk. We provide a closed-form expression for the rate and distortion for any k number of parallel descriptions and for any coding rate.

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Authors:
Ram Zamir
Submitted On:
24 March 2020 - 12:05pm
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[1] Ram Zamir, "The Exponential Distribution in Rate Distortion Theory: The Case of Compression with Independent Encodings", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5021. Accessed: Mar. 30, 2020.
@article{5021-20,
url = {http://sigport.org/5021},
author = {Ram Zamir },
publisher = {IEEE SigPort},
title = {The Exponential Distribution in Rate Distortion Theory: The Case of Compression with Independent Encodings},
year = {2020} }
TY - EJOUR
T1 - The Exponential Distribution in Rate Distortion Theory: The Case of Compression with Independent Encodings
AU - Ram Zamir
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5021
ER -
Ram Zamir. (2020). The Exponential Distribution in Rate Distortion Theory: The Case of Compression with Independent Encodings. IEEE SigPort. http://sigport.org/5021
Ram Zamir, 2020. The Exponential Distribution in Rate Distortion Theory: The Case of Compression with Independent Encodings. Available at: http://sigport.org/5021.
Ram Zamir. (2020). "The Exponential Distribution in Rate Distortion Theory: The Case of Compression with Independent Encodings." Web.
1. Ram Zamir. The Exponential Distribution in Rate Distortion Theory: The Case of Compression with Independent Encodings [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5021

Non-Binary Robust Universal Variable Length Codes


We extend the binary Fibonacci code to $d$-ary codes, with $d\ge 2$.
This is motivated by future technological developments in which the basic unit of storage will not be just a 2-valued bit, but possibly an element that is able to distinguish between $d$ different values.
The proposed codes are prefix-free, complete and more robust than Huffman codes. Experimental results illustrate that the compression efficiency of non-binary Fibonacci codes are very close to the savings achieved by the corresponding non-binary Huffman coding of the same order.

Paper Details

Authors:
Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira
Submitted On:
23 March 2020 - 10:58am
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[1] Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira, "Non-Binary Robust Universal Variable Length Codes", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5020. Accessed: Mar. 30, 2020.
@article{5020-20,
url = {http://sigport.org/5020},
author = {Shmuel T.\ Klein; Tamar C.\ Serebro; Dana Shapira },
publisher = {IEEE SigPort},
title = {Non-Binary Robust Universal Variable Length Codes},
year = {2020} }
TY - EJOUR
T1 - Non-Binary Robust Universal Variable Length Codes
AU - Shmuel T.\ Klein; Tamar C.\ Serebro; Dana Shapira
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5020
ER -
Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira. (2020). Non-Binary Robust Universal Variable Length Codes. IEEE SigPort. http://sigport.org/5020
Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira, 2020. Non-Binary Robust Universal Variable Length Codes. Available at: http://sigport.org/5020.
Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira. (2020). "Non-Binary Robust Universal Variable Length Codes." Web.
1. Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira. Non-Binary Robust Universal Variable Length Codes [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5020

Non-Binary Robust Universal Variable Length Codes


We extend the binary Fibonacci code to $d$-ary codes, with $d\ge 2$.
This is motivated by future technological developments in which the basic unit of storage will not be just a 2-valued bit, but possibly an element that is able to distinguish between $d$ different values.
The proposed codes are prefix-free, complete and more robust than Huffman codes. Experimental results illustrate that the compression efficiency of non-binary Fibonacci codes are very close to the savings achieved by the corresponding non-binary Huffman coding of the same order.

Paper Details

Authors:
Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira
Submitted On:
23 March 2020 - 10:58am
Short Link:
Type:
Event:

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Multiary_Fibonacci___I_C.pdf

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[1] Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira, "Non-Binary Robust Universal Variable Length Codes", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5019. Accessed: Mar. 30, 2020.
@article{5019-20,
url = {http://sigport.org/5019},
author = {Shmuel T.\ Klein; Tamar C.\ Serebro; Dana Shapira },
publisher = {IEEE SigPort},
title = {Non-Binary Robust Universal Variable Length Codes},
year = {2020} }
TY - EJOUR
T1 - Non-Binary Robust Universal Variable Length Codes
AU - Shmuel T.\ Klein; Tamar C.\ Serebro; Dana Shapira
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5019
ER -
Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira. (2020). Non-Binary Robust Universal Variable Length Codes. IEEE SigPort. http://sigport.org/5019
Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira, 2020. Non-Binary Robust Universal Variable Length Codes. Available at: http://sigport.org/5019.
Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira. (2020). "Non-Binary Robust Universal Variable Length Codes." Web.
1. Shmuel T.\ Klein, Tamar C.\ Serebro, Dana Shapira. Non-Binary Robust Universal Variable Length Codes [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5019

Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement


In this work, a flow-guided temporal-spatial network (FGTSN) is proposed to enhance the quality of HEVC compressed video. Specially, we first employ a robust motion estimation subnet via trainable optical flow module to estimate the motion flow between current frame and its adjacent frames. Guiding by the predicted motion flow, these adjacent frames are aligned to current frame. Then, a temporal encoder based on ConvLSTM with bidirectional residual structure is designed to discover the variations between the current frame and its warped frames.

Paper Details

Authors:
Xiandong Meng, Xuan Deng, Shuyuan Zhu, Shuaicheng Liu and Bing Zeng
Submitted On:
23 March 2020 - 7:14am
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[1] Xiandong Meng, Xuan Deng, Shuyuan Zhu, Shuaicheng Liu and Bing Zeng, "Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5018. Accessed: Mar. 30, 2020.
@article{5018-20,
url = {http://sigport.org/5018},
author = {Xiandong Meng; Xuan Deng; Shuyuan Zhu; Shuaicheng Liu and Bing Zeng },
publisher = {IEEE SigPort},
title = {Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement},
year = {2020} }
TY - EJOUR
T1 - Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement
AU - Xiandong Meng; Xuan Deng; Shuyuan Zhu; Shuaicheng Liu and Bing Zeng
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5018
ER -
Xiandong Meng, Xuan Deng, Shuyuan Zhu, Shuaicheng Liu and Bing Zeng. (2020). Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement. IEEE SigPort. http://sigport.org/5018
Xiandong Meng, Xuan Deng, Shuyuan Zhu, Shuaicheng Liu and Bing Zeng, 2020. Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement. Available at: http://sigport.org/5018.
Xiandong Meng, Xuan Deng, Shuyuan Zhu, Shuaicheng Liu and Bing Zeng. (2020). "Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement." Web.
1. Xiandong Meng, Xuan Deng, Shuyuan Zhu, Shuaicheng Liu and Bing Zeng. Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5018

Fast Multi-Rate Encoding for Adaptive HTTP Streaming


Adaptive HTTP streaming is the preferred method to deliver multimedia content in the internet. It provides multiple representations of the same content in different qualities (i.e. bit-rates and resolutions) and allows the client to request segments from the available representations in a dynamic, adaptive way depending on its context. The growing number of representations in adaptive HTTP streaming makes encoding of one video segment at different representations a challenging task in terms of encoding time-complexity.

Paper Details

Authors:
Hadi Amirpour, Ekrem Cetinkaya, Christian Timmerer, Mohammad Ghanbari
Submitted On:
23 March 2020 - 3:26am
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[1] Hadi Amirpour, Ekrem Cetinkaya, Christian Timmerer, Mohammad Ghanbari, "Fast Multi-Rate Encoding for Adaptive HTTP Streaming", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5017. Accessed: Mar. 30, 2020.
@article{5017-20,
url = {http://sigport.org/5017},
author = {Hadi Amirpour; Ekrem Cetinkaya; Christian Timmerer; Mohammad Ghanbari },
publisher = {IEEE SigPort},
title = {Fast Multi-Rate Encoding for Adaptive HTTP Streaming},
year = {2020} }
TY - EJOUR
T1 - Fast Multi-Rate Encoding for Adaptive HTTP Streaming
AU - Hadi Amirpour; Ekrem Cetinkaya; Christian Timmerer; Mohammad Ghanbari
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5017
ER -
Hadi Amirpour, Ekrem Cetinkaya, Christian Timmerer, Mohammad Ghanbari. (2020). Fast Multi-Rate Encoding for Adaptive HTTP Streaming. IEEE SigPort. http://sigport.org/5017
Hadi Amirpour, Ekrem Cetinkaya, Christian Timmerer, Mohammad Ghanbari, 2020. Fast Multi-Rate Encoding for Adaptive HTTP Streaming. Available at: http://sigport.org/5017.
Hadi Amirpour, Ekrem Cetinkaya, Christian Timmerer, Mohammad Ghanbari. (2020). "Fast Multi-Rate Encoding for Adaptive HTTP Streaming." Web.
1. Hadi Amirpour, Ekrem Cetinkaya, Christian Timmerer, Mohammad Ghanbari. Fast Multi-Rate Encoding for Adaptive HTTP Streaming [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5017

Online Probability Model Estimation for Video Compression


Modern video codec uses arithmetic coding for entropy coding. The arithmetic coding asymptotically achieves the entropy bound provided the true probability distribution. Hence the compression efficiency heavily relies on the ability to capture the time-variant probability model in video signals. Variants of first-order linear probability model update schemes have been used in recent generation video codecs. Built on top of those, a multimodal estimation scheme that forms a higher order probability model update has been proposed in this work.

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Authors:
Yue Sun, Jingning Han, Yaowu Xu
Submitted On:
22 March 2020 - 12:29pm
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[1] Yue Sun, Jingning Han, Yaowu Xu, "Online Probability Model Estimation for Video Compression", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5016. Accessed: Mar. 30, 2020.
@article{5016-20,
url = {http://sigport.org/5016},
author = {Yue Sun; Jingning Han; Yaowu Xu },
publisher = {IEEE SigPort},
title = {Online Probability Model Estimation for Video Compression},
year = {2020} }
TY - EJOUR
T1 - Online Probability Model Estimation for Video Compression
AU - Yue Sun; Jingning Han; Yaowu Xu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5016
ER -
Yue Sun, Jingning Han, Yaowu Xu. (2020). Online Probability Model Estimation for Video Compression. IEEE SigPort. http://sigport.org/5016
Yue Sun, Jingning Han, Yaowu Xu, 2020. Online Probability Model Estimation for Video Compression. Available at: http://sigport.org/5016.
Yue Sun, Jingning Han, Yaowu Xu. (2020). "Online Probability Model Estimation for Video Compression." Web.
1. Yue Sun, Jingning Han, Yaowu Xu. Online Probability Model Estimation for Video Compression [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5016

Binary Representation and High Efficient Compression of 3D CNN Features for Action Recognition

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Authors:
Peiyin Xing, Peixin Peng, Yongsheng Liang, Tiejun Huang, Yonghong Tian
Submitted On:
22 March 2020 - 4:34am
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[1] Peiyin Xing, Peixin Peng, Yongsheng Liang, Tiejun Huang, Yonghong Tian, "Binary Representation and High Efficient Compression of 3D CNN Features for Action Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5015. Accessed: Mar. 30, 2020.
@article{5015-20,
url = {http://sigport.org/5015},
author = {Peiyin Xing; Peixin Peng; Yongsheng Liang; Tiejun Huang; Yonghong Tian },
publisher = {IEEE SigPort},
title = {Binary Representation and High Efficient Compression of 3D CNN Features for Action Recognition},
year = {2020} }
TY - EJOUR
T1 - Binary Representation and High Efficient Compression of 3D CNN Features for Action Recognition
AU - Peiyin Xing; Peixin Peng; Yongsheng Liang; Tiejun Huang; Yonghong Tian
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5015
ER -
Peiyin Xing, Peixin Peng, Yongsheng Liang, Tiejun Huang, Yonghong Tian. (2020). Binary Representation and High Efficient Compression of 3D CNN Features for Action Recognition. IEEE SigPort. http://sigport.org/5015
Peiyin Xing, Peixin Peng, Yongsheng Liang, Tiejun Huang, Yonghong Tian, 2020. Binary Representation and High Efficient Compression of 3D CNN Features for Action Recognition. Available at: http://sigport.org/5015.
Peiyin Xing, Peixin Peng, Yongsheng Liang, Tiejun Huang, Yonghong Tian. (2020). "Binary Representation and High Efficient Compression of 3D CNN Features for Action Recognition." Web.
1. Peiyin Xing, Peixin Peng, Yongsheng Liang, Tiejun Huang, Yonghong Tian. Binary Representation and High Efficient Compression of 3D CNN Features for Action Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5015

Revisiting compact RDF stores based on k2-trees

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Authors:
Nieves R. Brisaboa, Ana Cerdeira-Pena, Guillermo de Bernardo, Antonio Fariña
Submitted On:
26 March 2020 - 7:19am
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[1] Nieves R. Brisaboa, Ana Cerdeira-Pena, Guillermo de Bernardo, Antonio Fariña, "Revisiting compact RDF stores based on k2-trees", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5014. Accessed: Mar. 30, 2020.
@article{5014-20,
url = {http://sigport.org/5014},
author = {Nieves R. Brisaboa; Ana Cerdeira-Pena; Guillermo de Bernardo; Antonio Fariña },
publisher = {IEEE SigPort},
title = {Revisiting compact RDF stores based on k2-trees},
year = {2020} }
TY - EJOUR
T1 - Revisiting compact RDF stores based on k2-trees
AU - Nieves R. Brisaboa; Ana Cerdeira-Pena; Guillermo de Bernardo; Antonio Fariña
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5014
ER -
Nieves R. Brisaboa, Ana Cerdeira-Pena, Guillermo de Bernardo, Antonio Fariña. (2020). Revisiting compact RDF stores based on k2-trees. IEEE SigPort. http://sigport.org/5014
Nieves R. Brisaboa, Ana Cerdeira-Pena, Guillermo de Bernardo, Antonio Fariña, 2020. Revisiting compact RDF stores based on k2-trees. Available at: http://sigport.org/5014.
Nieves R. Brisaboa, Ana Cerdeira-Pena, Guillermo de Bernardo, Antonio Fariña. (2020). "Revisiting compact RDF stores based on k2-trees." Web.
1. Nieves R. Brisaboa, Ana Cerdeira-Pena, Guillermo de Bernardo, Antonio Fariña. Revisiting compact RDF stores based on k2-trees [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5014

Convolutional Neural Network-Based Coefficients Prediction for HEVC Intra-Predicted Residues

Paper Details

Authors:
Dong Liu, Li Li, Yao Wang, Feng Wu
Submitted On:
20 March 2020 - 11:03pm
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[1] Dong Liu, Li Li, Yao Wang, Feng Wu, "Convolutional Neural Network-Based Coefficients Prediction for HEVC Intra-Predicted Residues", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5013. Accessed: Mar. 30, 2020.
@article{5013-20,
url = {http://sigport.org/5013},
author = {Dong Liu; Li Li; Yao Wang; Feng Wu },
publisher = {IEEE SigPort},
title = {Convolutional Neural Network-Based Coefficients Prediction for HEVC Intra-Predicted Residues},
year = {2020} }
TY - EJOUR
T1 - Convolutional Neural Network-Based Coefficients Prediction for HEVC Intra-Predicted Residues
AU - Dong Liu; Li Li; Yao Wang; Feng Wu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5013
ER -
Dong Liu, Li Li, Yao Wang, Feng Wu. (2020). Convolutional Neural Network-Based Coefficients Prediction for HEVC Intra-Predicted Residues. IEEE SigPort. http://sigport.org/5013
Dong Liu, Li Li, Yao Wang, Feng Wu, 2020. Convolutional Neural Network-Based Coefficients Prediction for HEVC Intra-Predicted Residues. Available at: http://sigport.org/5013.
Dong Liu, Li Li, Yao Wang, Feng Wu. (2020). "Convolutional Neural Network-Based Coefficients Prediction for HEVC Intra-Predicted Residues." Web.
1. Dong Liu, Li Li, Yao Wang, Feng Wu. Convolutional Neural Network-Based Coefficients Prediction for HEVC Intra-Predicted Residues [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5013

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