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GlobalSIP 2018

The 6th IEEE Global Conference on Signal and Information Processing (GlobalSIP)  focuses on signal and information processing with an emphasis on up-and-coming signal processing themes. The conference features world-class plenary speeches, distinguished symposium talks, tutorials, exhibits, oral and poster sessions, and panels. GlobalSIP is comprised of co-located General Symposium and symposia selected based on responses to the call-for-symposia proposals.

RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES

Hayoung Choi

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Hayoung Choi
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26 November 2018 - 3:06am
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Poster for GlobalSIP

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[1] , "RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3776. Accessed: Feb. 22, 2019.
@article{3776-18,
url = {http://sigport.org/3776},
author = { },
publisher = {IEEE SigPort},
title = {RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES},
year = {2018} }
TY - EJOUR
T1 - RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3776
ER -
. (2018). RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES. IEEE SigPort. http://sigport.org/3776
, 2018. RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES. Available at: http://sigport.org/3776.
. (2018). "RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES." Web.
1. . RANDOMIZED METHOD FOR ESTIMATING THE VON NEUMANN ENTROPY OF LARGE-SCALE DENSITY MATRICES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3776

SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS

Shashini De silva

Time-synchronized phasor measurements from phasor measurement units (PMUs) are valuable for real time monitoring and control. However, their reliance on civilian GPS signals makes them vulnerable to GPS signal spoofing attacks which can be launched by an adversary to falsify PMU data entries.

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Authors:
Shashini De silva
Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez
Submitted On:
25 November 2018 - 9:43pm
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[1] Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez, "SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3775. Accessed: Feb. 22, 2019.
@article{3775-18,
url = {http://sigport.org/3775},
author = {Shashini De Silva; Travis Hagen; Jinsub Kim; Eduardo Cotilla-Sanchez },
publisher = {IEEE SigPort},
title = {SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS},
year = {2018} }
TY - EJOUR
T1 - SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS
AU - Shashini De Silva; Travis Hagen; Jinsub Kim; Eduardo Cotilla-Sanchez
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3775
ER -
Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez. (2018). SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS. IEEE SigPort. http://sigport.org/3775
Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez, 2018. SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS. Available at: http://sigport.org/3775.
Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez. (2018). "SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS." Web.
1. Shashini De Silva, Travis Hagen, Jinsub Kim, Eduardo Cotilla-Sanchez. SPARSE ERROR CORRECTION FOR PMU DATA UNDER GPS SPOOFING ATTACKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3775

RANDOM ENSEMBLE OF LOCALLY OPTIMUM DETECTORS FOR DETECTION OF ADVERSARIAL EXAMPLES

Amish Goel

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Authors:
Amish Goel
Amish Goel, Pierre Moulin
Submitted On:
25 November 2018 - 12:48pm
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Poster_GlobalSIP.pdf

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[1] Amish Goel, Pierre Moulin, "RANDOM ENSEMBLE OF LOCALLY OPTIMUM DETECTORS FOR DETECTION OF ADVERSARIAL EXAMPLES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3774. Accessed: Feb. 22, 2019.
@article{3774-18,
url = {http://sigport.org/3774},
author = {Amish Goel; Pierre Moulin },
publisher = {IEEE SigPort},
title = {RANDOM ENSEMBLE OF LOCALLY OPTIMUM DETECTORS FOR DETECTION OF ADVERSARIAL EXAMPLES},
year = {2018} }
TY - EJOUR
T1 - RANDOM ENSEMBLE OF LOCALLY OPTIMUM DETECTORS FOR DETECTION OF ADVERSARIAL EXAMPLES
AU - Amish Goel; Pierre Moulin
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3774
ER -
Amish Goel, Pierre Moulin. (2018). RANDOM ENSEMBLE OF LOCALLY OPTIMUM DETECTORS FOR DETECTION OF ADVERSARIAL EXAMPLES. IEEE SigPort. http://sigport.org/3774
Amish Goel, Pierre Moulin, 2018. RANDOM ENSEMBLE OF LOCALLY OPTIMUM DETECTORS FOR DETECTION OF ADVERSARIAL EXAMPLES. Available at: http://sigport.org/3774.
Amish Goel, Pierre Moulin. (2018). "RANDOM ENSEMBLE OF LOCALLY OPTIMUM DETECTORS FOR DETECTION OF ADVERSARIAL EXAMPLES." Web.
1. Amish Goel, Pierre Moulin. RANDOM ENSEMBLE OF LOCALLY OPTIMUM DETECTORS FOR DETECTION OF ADVERSARIAL EXAMPLES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3774

Coincident Peak Prediction Using a Feed-Forward Neural Network

Chase Dowling

A significant portion of a business' annual electrical payments can be made up of coincident peak charges: a transmission surcharge for power consumed when the entire system is at peak demand. This charge occurs only a few times annually, but with per-MW prices orders of magnitudes higher than non-peak times. A business is incentivized to reduce its power consumption, but accurately predicting the timing of peak demand charges is nontrivial. In this paper we present a decision framework based on predicting the day-ahead likelihood of peak demand charges.

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Authors:
Chase Dowling
Daniel Kirschen, Baosen Zhang
Submitted On:
28 November 2018 - 2:08pm
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Up-to-date slides.

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Coincident_Peak_Prediction_Slides.pdf

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[1] Daniel Kirschen, Baosen Zhang, "Coincident Peak Prediction Using a Feed-Forward Neural Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3773. Accessed: Feb. 22, 2019.
@article{3773-18,
url = {http://sigport.org/3773},
author = {Daniel Kirschen; Baosen Zhang },
publisher = {IEEE SigPort},
title = {Coincident Peak Prediction Using a Feed-Forward Neural Network},
year = {2018} }
TY - EJOUR
T1 - Coincident Peak Prediction Using a Feed-Forward Neural Network
AU - Daniel Kirschen; Baosen Zhang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3773
ER -
Daniel Kirschen, Baosen Zhang. (2018). Coincident Peak Prediction Using a Feed-Forward Neural Network. IEEE SigPort. http://sigport.org/3773
Daniel Kirschen, Baosen Zhang, 2018. Coincident Peak Prediction Using a Feed-Forward Neural Network. Available at: http://sigport.org/3773.
Daniel Kirschen, Baosen Zhang. (2018). "Coincident Peak Prediction Using a Feed-Forward Neural Network." Web.
1. Daniel Kirschen, Baosen Zhang. Coincident Peak Prediction Using a Feed-Forward Neural Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3773

A MACHINE LEARNING APPROACH FOR THE CLASSIFICATION OF INDOOR ENVIRONMENTS USING RF SIGNATURES

Mohamed AlHajri

Efficient deployment of Internet of Things (IoT) sensors primarily depends on allowing the adjustment of sensor power consumption according to the radio frequency (RF) propagation channel which is dictated by the type of the surrounding indoor environment. This paper develops a machine learning approach for indoor environment classification by exploiting support vector machine (SVM) based on RF signatures computed from real-time measurements.

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Authors:
Mohamed AlHajri
Mohamed I. AlHajri, Nazar T. Ali, Raed M. Shubair
Submitted On:
24 November 2018 - 4:04pm
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GlobalSIP_Poster.pdf

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[1] Mohamed I. AlHajri, Nazar T. Ali, Raed M. Shubair, "A MACHINE LEARNING APPROACH FOR THE CLASSIFICATION OF INDOOR ENVIRONMENTS USING RF SIGNATURES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3772. Accessed: Feb. 22, 2019.
@article{3772-18,
url = {http://sigport.org/3772},
author = {Mohamed I. AlHajri; Nazar T. Ali; Raed M. Shubair },
publisher = {IEEE SigPort},
title = {A MACHINE LEARNING APPROACH FOR THE CLASSIFICATION OF INDOOR ENVIRONMENTS USING RF SIGNATURES},
year = {2018} }
TY - EJOUR
T1 - A MACHINE LEARNING APPROACH FOR THE CLASSIFICATION OF INDOOR ENVIRONMENTS USING RF SIGNATURES
AU - Mohamed I. AlHajri; Nazar T. Ali; Raed M. Shubair
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3772
ER -
Mohamed I. AlHajri, Nazar T. Ali, Raed M. Shubair. (2018). A MACHINE LEARNING APPROACH FOR THE CLASSIFICATION OF INDOOR ENVIRONMENTS USING RF SIGNATURES. IEEE SigPort. http://sigport.org/3772
Mohamed I. AlHajri, Nazar T. Ali, Raed M. Shubair, 2018. A MACHINE LEARNING APPROACH FOR THE CLASSIFICATION OF INDOOR ENVIRONMENTS USING RF SIGNATURES. Available at: http://sigport.org/3772.
Mohamed I. AlHajri, Nazar T. Ali, Raed M. Shubair. (2018). "A MACHINE LEARNING APPROACH FOR THE CLASSIFICATION OF INDOOR ENVIRONMENTS USING RF SIGNATURES." Web.
1. Mohamed I. AlHajri, Nazar T. Ali, Raed M. Shubair. A MACHINE LEARNING APPROACH FOR THE CLASSIFICATION OF INDOOR ENVIRONMENTS USING RF SIGNATURES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3772

BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS

Samrat Nath

We study the quickest change point detection for systems with multiple possible post-change models. A change point is the time instant at which the distribution of a random process changes. We consider the case that the post-change model is from a finite set of possible models. Under the Bayesian setting, the objective is to minimize the average detection delay (ADD), subject to upper bounds on the probability of false alarm (PFA). The proposed algorithm is a threshold-based sequential test.

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Authors:
Samrat Nath
Samrat Nath, Jingxian Wu
Submitted On:
24 November 2018 - 2:22pm
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Change_Detection_GlobalSIP18.pdf

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[1] Samrat Nath, Jingxian Wu, "BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3771. Accessed: Feb. 22, 2019.
@article{3771-18,
url = {http://sigport.org/3771},
author = {Samrat Nath; Jingxian Wu },
publisher = {IEEE SigPort},
title = {BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS},
year = {2018} }
TY - EJOUR
T1 - BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS
AU - Samrat Nath; Jingxian Wu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3771
ER -
Samrat Nath, Jingxian Wu. (2018). BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS. IEEE SigPort. http://sigport.org/3771
Samrat Nath, Jingxian Wu, 2018. BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS. Available at: http://sigport.org/3771.
Samrat Nath, Jingxian Wu. (2018). "BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS." Web.
1. Samrat Nath, Jingxian Wu. BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3771

BANDWIDTH AND ROUND-TRIP TIME DETECTION BASED CONGESTION CONTROL FOR MULTIPATH TCP OVER HIGHLY LOSSY SATELLITE NETWORKS

heng li

Satellite systems might become an important supplement to 5G systems. In addition, compared with regular TCP, multipath TCP (MPTCP) provides a robust solution. However, due to the high latency and high loss of satellite networks, most of the existing multipath TCP congestion control schemes do not perform well. Therefore, we proposed a bandwidth and round-trip time detection-based congestion control (BWRD) for multipath TCP over highly lossy satellite networks. Simulations are running to evaluate the proposed BWRD algorithm.

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Authors:
heng li
Hefei Hu, Yuanan Liu
Submitted On:
24 November 2018 - 9:50am
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Global SIP Poster

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[1] Hefei Hu, Yuanan Liu, "BANDWIDTH AND ROUND-TRIP TIME DETECTION BASED CONGESTION CONTROL FOR MULTIPATH TCP OVER HIGHLY LOSSY SATELLITE NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3770. Accessed: Feb. 22, 2019.
@article{3770-18,
url = {http://sigport.org/3770},
author = {Hefei Hu; Yuanan Liu },
publisher = {IEEE SigPort},
title = {BANDWIDTH AND ROUND-TRIP TIME DETECTION BASED CONGESTION CONTROL FOR MULTIPATH TCP OVER HIGHLY LOSSY SATELLITE NETWORKS},
year = {2018} }
TY - EJOUR
T1 - BANDWIDTH AND ROUND-TRIP TIME DETECTION BASED CONGESTION CONTROL FOR MULTIPATH TCP OVER HIGHLY LOSSY SATELLITE NETWORKS
AU - Hefei Hu; Yuanan Liu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3770
ER -
Hefei Hu, Yuanan Liu. (2018). BANDWIDTH AND ROUND-TRIP TIME DETECTION BASED CONGESTION CONTROL FOR MULTIPATH TCP OVER HIGHLY LOSSY SATELLITE NETWORKS. IEEE SigPort. http://sigport.org/3770
Hefei Hu, Yuanan Liu, 2018. BANDWIDTH AND ROUND-TRIP TIME DETECTION BASED CONGESTION CONTROL FOR MULTIPATH TCP OVER HIGHLY LOSSY SATELLITE NETWORKS. Available at: http://sigport.org/3770.
Hefei Hu, Yuanan Liu. (2018). "BANDWIDTH AND ROUND-TRIP TIME DETECTION BASED CONGESTION CONTROL FOR MULTIPATH TCP OVER HIGHLY LOSSY SATELLITE NETWORKS." Web.
1. Hefei Hu, Yuanan Liu. BANDWIDTH AND ROUND-TRIP TIME DETECTION BASED CONGESTION CONTROL FOR MULTIPATH TCP OVER HIGHLY LOSSY SATELLITE NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3770

Defending DNN Adversarial Attacks with Pruning and Logits Augmentation

Siyue Wang

Deep neural networks (DNNs) have been shown to be powerful models and perform extremely well on many complicated artificial intelligent tasks. However, recent research found that these powerful models are vulnerable to adversarial attacks, i.e., intentionally added imperceptible perturbations to DNN inputs can easily mislead the DNNs with extremely high confidence. In this work, we enhance the robustness of DNNs under adversarial attacks by using pruning method and logits augmentation, we achieve both effective defense against adversarial examples and DNN model compression.

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Authors:
Siyue Wang
Xiao Wang, Shaokai Ye, Pu Zhao, Xue Lin
Submitted On:
24 November 2018 - 8:54am
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GlobalSip_Final.pdf

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[1] Xiao Wang, Shaokai Ye, Pu Zhao, Xue Lin, "Defending DNN Adversarial Attacks with Pruning and Logits Augmentation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3769. Accessed: Feb. 22, 2019.
@article{3769-18,
url = {http://sigport.org/3769},
author = {Xiao Wang; Shaokai Ye; Pu Zhao; Xue Lin },
publisher = {IEEE SigPort},
title = {Defending DNN Adversarial Attacks with Pruning and Logits Augmentation},
year = {2018} }
TY - EJOUR
T1 - Defending DNN Adversarial Attacks with Pruning and Logits Augmentation
AU - Xiao Wang; Shaokai Ye; Pu Zhao; Xue Lin
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3769
ER -
Xiao Wang, Shaokai Ye, Pu Zhao, Xue Lin. (2018). Defending DNN Adversarial Attacks with Pruning and Logits Augmentation. IEEE SigPort. http://sigport.org/3769
Xiao Wang, Shaokai Ye, Pu Zhao, Xue Lin, 2018. Defending DNN Adversarial Attacks with Pruning and Logits Augmentation. Available at: http://sigport.org/3769.
Xiao Wang, Shaokai Ye, Pu Zhao, Xue Lin. (2018). "Defending DNN Adversarial Attacks with Pruning and Logits Augmentation." Web.
1. Xiao Wang, Shaokai Ye, Pu Zhao, Xue Lin. Defending DNN Adversarial Attacks with Pruning and Logits Augmentation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3769

LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING

Zachary Lee

Large-scale charging infrastructure will play an important role in supporting the adoption of electric vehicles. In this presentation, we describe a unique physical testbed for large-scale, high-density EV charging research which we call the Adaptive Charging Network (ACN). We describe the architecture of the ACN including its hardware and software components. We also present a practical framework for online scheduling, which is based on model predictive control and convex optimization.

Paper Details

Authors:
Zachary Lee
Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low
Submitted On:
24 November 2018 - 3:10am
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Global SIP Presentation.pdf

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[1] Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low, "LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3768. Accessed: Feb. 22, 2019.
@article{3768-18,
url = {http://sigport.org/3768},
author = {Zachary J. Lee; Daniel Chang; Cheng Jin; George S. Lee; Rand Lee; Ted Lee; Steven H. Low },
publisher = {IEEE SigPort},
title = {LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING},
year = {2018} }
TY - EJOUR
T1 - LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING
AU - Zachary J. Lee; Daniel Chang; Cheng Jin; George S. Lee; Rand Lee; Ted Lee; Steven H. Low
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3768
ER -
Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low. (2018). LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING. IEEE SigPort. http://sigport.org/3768
Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low, 2018. LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING. Available at: http://sigport.org/3768.
Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low. (2018). "LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING." Web.
1. Zachary J. Lee, Daniel Chang, Cheng Jin, George S. Lee, Rand Lee, Ted Lee, Steven H. Low. LARGE-SCALE ADAPTIVE ELECTRIC VEHICLE CHARGING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3768

PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING

Yasuko Sugito

We evaluate the performance of objective quality metrics for high dynamic range (HDR) image coding that uses the transfer function (TF) of the Hybrid Log-Gamma (HLG) method. Previous evaluations of objective metrics for HDR image coding have studied which of them are reliable predictors of perceived quality; however, in those tests, all the non-linear transforms used both for encoding and by the best-performing metrics are essentially very similar and based on visual perception data of detection thresholds for lightness variations.

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Authors:
Yasuko Sugito
Marcelo Bertalmío
Submitted On:
29 November 2018 - 9:25pm
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20181129_GlobalSIP_Yasuko.pdf

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[1] Marcelo Bertalmío, "PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3767. Accessed: Feb. 22, 2019.
@article{3767-18,
url = {http://sigport.org/3767},
author = {Marcelo Bertalmío },
publisher = {IEEE SigPort},
title = {PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING},
year = {2018} }
TY - EJOUR
T1 - PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING
AU - Marcelo Bertalmío
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3767
ER -
Marcelo Bertalmío. (2018). PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING. IEEE SigPort. http://sigport.org/3767
Marcelo Bertalmío, 2018. PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING. Available at: http://sigport.org/3767.
Marcelo Bertalmío. (2018). "PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING." Web.
1. Marcelo Bertalmío. PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3767

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