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ICIP 2020

ICIP 2020 is a fully virtual conference. The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website

Automatic Region Selection For Objective Sharpness Assessment of Mobile Device Photos


Mobile devices are the source of a vast majority of digital photos today. Photos taken by mobile devices generally have fairly good visual quality. When evaluating high-quality mobile device photos, people have to manually zoom in to local regions to discern the subtle difference. Understandably, a global objective quality assessment method cannot perform well on such task. Therefore, local region selection is widely recognized as a prerequisite for the following quality evaluation.

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Authors:
Guangtao Zhai, Wenhan Zhu, Yucheng Zhu, Xiongkuo Min, Xiao-Ping Zhang, Hua Yang
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2 November 2020 - 9:31pm
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[1] Guangtao Zhai, Wenhan Zhu, Yucheng Zhu, Xiongkuo Min, Xiao-Ping Zhang, Hua Yang, "Automatic Region Selection For Objective Sharpness Assessment of Mobile Device Photos", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5497. Accessed: Nov. 26, 2020.
@article{5497-20,
url = {http://sigport.org/5497},
author = {Guangtao Zhai; Wenhan Zhu; Yucheng Zhu; Xiongkuo Min; Xiao-Ping Zhang; Hua Yang },
publisher = {IEEE SigPort},
title = {Automatic Region Selection For Objective Sharpness Assessment of Mobile Device Photos},
year = {2020} }
TY - EJOUR
T1 - Automatic Region Selection For Objective Sharpness Assessment of Mobile Device Photos
AU - Guangtao Zhai; Wenhan Zhu; Yucheng Zhu; Xiongkuo Min; Xiao-Ping Zhang; Hua Yang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5497
ER -
Guangtao Zhai, Wenhan Zhu, Yucheng Zhu, Xiongkuo Min, Xiao-Ping Zhang, Hua Yang. (2020). Automatic Region Selection For Objective Sharpness Assessment of Mobile Device Photos. IEEE SigPort. http://sigport.org/5497
Guangtao Zhai, Wenhan Zhu, Yucheng Zhu, Xiongkuo Min, Xiao-Ping Zhang, Hua Yang, 2020. Automatic Region Selection For Objective Sharpness Assessment of Mobile Device Photos. Available at: http://sigport.org/5497.
Guangtao Zhai, Wenhan Zhu, Yucheng Zhu, Xiongkuo Min, Xiao-Ping Zhang, Hua Yang. (2020). "Automatic Region Selection For Objective Sharpness Assessment of Mobile Device Photos." Web.
1. Guangtao Zhai, Wenhan Zhu, Yucheng Zhu, Xiongkuo Min, Xiao-Ping Zhang, Hua Yang. Automatic Region Selection For Objective Sharpness Assessment of Mobile Device Photos [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5497

SELECTIVE COMPLEMENTARY FEATURES FOR MULTI-PERSON POSE ESTIMATION

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2 November 2020 - 9:03pm
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[1] , "SELECTIVE COMPLEMENTARY FEATURES FOR MULTI-PERSON POSE ESTIMATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5496. Accessed: Nov. 26, 2020.
@article{5496-20,
url = {http://sigport.org/5496},
author = { },
publisher = {IEEE SigPort},
title = {SELECTIVE COMPLEMENTARY FEATURES FOR MULTI-PERSON POSE ESTIMATION},
year = {2020} }
TY - EJOUR
T1 - SELECTIVE COMPLEMENTARY FEATURES FOR MULTI-PERSON POSE ESTIMATION
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5496
ER -
. (2020). SELECTIVE COMPLEMENTARY FEATURES FOR MULTI-PERSON POSE ESTIMATION. IEEE SigPort. http://sigport.org/5496
, 2020. SELECTIVE COMPLEMENTARY FEATURES FOR MULTI-PERSON POSE ESTIMATION. Available at: http://sigport.org/5496.
. (2020). "SELECTIVE COMPLEMENTARY FEATURES FOR MULTI-PERSON POSE ESTIMATION." Web.
1. . SELECTIVE COMPLEMENTARY FEATURES FOR MULTI-PERSON POSE ESTIMATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5496

A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment


Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores. However, less attention has been paid to studying the relative efficiencies of different pooling methods on no-reference (blind) VQA. Here we conduct a large-scale comparative evaluation to assess the capabilities and limitations of multiple temporal pooling strategies on blind VQA of user-generated videos. The study yields insights and general guidance regarding the application and selection of temporal pooling models.

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Authors:
Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik
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2 November 2020 - 8:00pm
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[1] Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik, "A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5495. Accessed: Nov. 26, 2020.
@article{5495-20,
url = {http://sigport.org/5495},
author = {Zhengzhong Tu; Chia-Ju Chen; Li-Heng Chen; Neil Birkbeck; Balu Adsumilli; Alan C. Bovik },
publisher = {IEEE SigPort},
title = {A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment},
year = {2020} }
TY - EJOUR
T1 - A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment
AU - Zhengzhong Tu; Chia-Ju Chen; Li-Heng Chen; Neil Birkbeck; Balu Adsumilli; Alan C. Bovik
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5495
ER -
Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik. (2020). A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment. IEEE SigPort. http://sigport.org/5495
Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik, 2020. A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment. Available at: http://sigport.org/5495.
Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik. (2020). "A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment." Web.
1. Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen, Neil Birkbeck, Balu Adsumilli, Alan C. Bovik. A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5495

JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT CONVOLUTIONAL NEURAL NETWORKS WITH COUPLED SPARSE PRIORS

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Authors:
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis
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2 November 2020 - 6:04pm
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[1] Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis, "JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT CONVOLUTIONAL NEURAL NETWORKS WITH COUPLED SPARSE PRIORS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5494. Accessed: Nov. 26, 2020.
@article{5494-20,
url = {http://sigport.org/5494},
author = {Iman Marivani; Evaggelia Tsiligianni; Bruno Cornelis; Nikos Deligiannis },
publisher = {IEEE SigPort},
title = {JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT CONVOLUTIONAL NEURAL NETWORKS WITH COUPLED SPARSE PRIORS},
year = {2020} }
TY - EJOUR
T1 - JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT CONVOLUTIONAL NEURAL NETWORKS WITH COUPLED SPARSE PRIORS
AU - Iman Marivani; Evaggelia Tsiligianni; Bruno Cornelis; Nikos Deligiannis
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5494
ER -
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis. (2020). JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT CONVOLUTIONAL NEURAL NETWORKS WITH COUPLED SPARSE PRIORS. IEEE SigPort. http://sigport.org/5494
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis, 2020. JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT CONVOLUTIONAL NEURAL NETWORKS WITH COUPLED SPARSE PRIORS. Available at: http://sigport.org/5494.
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis. (2020). "JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT CONVOLUTIONAL NEURAL NETWORKS WITH COUPLED SPARSE PRIORS." Web.
1. Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis. JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT CONVOLUTIONAL NEURAL NETWORKS WITH COUPLED SPARSE PRIORS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5494

DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK

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Authors:
Mohammad Madine, Islem Rekik, Naoufel Werghi
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2 November 2020 - 5:32pm
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DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING.pdf

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[1] Mohammad Madine, Islem Rekik, Naoufel Werghi, "DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5493. Accessed: Nov. 26, 2020.
@article{5493-20,
url = {http://sigport.org/5493},
author = {Mohammad Madine; Islem Rekik; Naoufel Werghi },
publisher = {IEEE SigPort},
title = {DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK},
year = {2020} }
TY - EJOUR
T1 - DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK
AU - Mohammad Madine; Islem Rekik; Naoufel Werghi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5493
ER -
Mohammad Madine, Islem Rekik, Naoufel Werghi. (2020). DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK. IEEE SigPort. http://sigport.org/5493
Mohammad Madine, Islem Rekik, Naoufel Werghi, 2020. DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK. Available at: http://sigport.org/5493.
Mohammad Madine, Islem Rekik, Naoufel Werghi. (2020). "DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK." Web.
1. Mohammad Madine, Islem Rekik, Naoufel Werghi. DIAGNOSING AUTISM USING T1-W MRI WITH MULTI-KERNEL LEARNING AND HYPERGRAPH NEURAL NETWORK [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5493

DEEP LEARNING VS. TRADITIONAL ALGORITHMS FOR SALIENCY PREDICTION OF DISTORTED IMAGES


Saliency has been widely studied in relation to image quality assessment (IQA). The optimal use of saliency in IQA metrics, however, is nontrivial and largely depends on whether saliency can be accurately predicted for images containing various distortions. Although tremendous progress has been made in saliency modelling, very little is known about whether and to what extent state-of-the-art methods are beneficial for saliency prediction of distorted images.

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Authors:
Xin Zhao, Hanhe Lin, Pengfei Guo, Dietmar Saupe, Hantao Liu
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2 November 2020 - 5:25pm
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[1] Xin Zhao, Hanhe Lin, Pengfei Guo, Dietmar Saupe, Hantao Liu, "DEEP LEARNING VS. TRADITIONAL ALGORITHMS FOR SALIENCY PREDICTION OF DISTORTED IMAGES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5492. Accessed: Nov. 26, 2020.
@article{5492-20,
url = {http://sigport.org/5492},
author = {Xin Zhao; Hanhe Lin; Pengfei Guo; Dietmar Saupe; Hantao Liu },
publisher = {IEEE SigPort},
title = {DEEP LEARNING VS. TRADITIONAL ALGORITHMS FOR SALIENCY PREDICTION OF DISTORTED IMAGES},
year = {2020} }
TY - EJOUR
T1 - DEEP LEARNING VS. TRADITIONAL ALGORITHMS FOR SALIENCY PREDICTION OF DISTORTED IMAGES
AU - Xin Zhao; Hanhe Lin; Pengfei Guo; Dietmar Saupe; Hantao Liu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5492
ER -
Xin Zhao, Hanhe Lin, Pengfei Guo, Dietmar Saupe, Hantao Liu. (2020). DEEP LEARNING VS. TRADITIONAL ALGORITHMS FOR SALIENCY PREDICTION OF DISTORTED IMAGES. IEEE SigPort. http://sigport.org/5492
Xin Zhao, Hanhe Lin, Pengfei Guo, Dietmar Saupe, Hantao Liu, 2020. DEEP LEARNING VS. TRADITIONAL ALGORITHMS FOR SALIENCY PREDICTION OF DISTORTED IMAGES. Available at: http://sigport.org/5492.
Xin Zhao, Hanhe Lin, Pengfei Guo, Dietmar Saupe, Hantao Liu. (2020). "DEEP LEARNING VS. TRADITIONAL ALGORITHMS FOR SALIENCY PREDICTION OF DISTORTED IMAGES." Web.
1. Xin Zhao, Hanhe Lin, Pengfei Guo, Dietmar Saupe, Hantao Liu. DEEP LEARNING VS. TRADITIONAL ALGORITHMS FOR SALIENCY PREDICTION OF DISTORTED IMAGES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5492

Spatial Keyframe Extraction of Mobile Videos for Efficient Object Detection at the Edge


Advances in federated learning and edge computing advocate for deep learning models to run at edge devices for video analysis. However, the captured video frame rate is too high to be processed at the edge in real-time with a typical model such as CNN. Any approach to consecutively feed frames to the model compromises both the quality (by missing important frames) and the efficiency (by processing redundantly similar frames) of analysis.

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Authors:
George Constantinou, Cyrus Shahabi, Seon Ho Kim
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2 November 2020 - 4:52pm
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[1] George Constantinou, Cyrus Shahabi, Seon Ho Kim, "Spatial Keyframe Extraction of Mobile Videos for Efficient Object Detection at the Edge", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5490. Accessed: Nov. 26, 2020.
@article{5490-20,
url = {http://sigport.org/5490},
author = {George Constantinou; Cyrus Shahabi; Seon Ho Kim },
publisher = {IEEE SigPort},
title = {Spatial Keyframe Extraction of Mobile Videos for Efficient Object Detection at the Edge},
year = {2020} }
TY - EJOUR
T1 - Spatial Keyframe Extraction of Mobile Videos for Efficient Object Detection at the Edge
AU - George Constantinou; Cyrus Shahabi; Seon Ho Kim
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5490
ER -
George Constantinou, Cyrus Shahabi, Seon Ho Kim. (2020). Spatial Keyframe Extraction of Mobile Videos for Efficient Object Detection at the Edge. IEEE SigPort. http://sigport.org/5490
George Constantinou, Cyrus Shahabi, Seon Ho Kim, 2020. Spatial Keyframe Extraction of Mobile Videos for Efficient Object Detection at the Edge. Available at: http://sigport.org/5490.
George Constantinou, Cyrus Shahabi, Seon Ho Kim. (2020). "Spatial Keyframe Extraction of Mobile Videos for Efficient Object Detection at the Edge." Web.
1. George Constantinou, Cyrus Shahabi, Seon Ho Kim. Spatial Keyframe Extraction of Mobile Videos for Efficient Object Detection at the Edge [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5490

Kernelized Dense Layers For Facial Expression Recognition


Fully connected layer is an essential component of Convolutional Neural Networks (CNNs), which demonstrates its efficiency in computer vision tasks. The CNN process usually starts with convolution and pooling layers that first break down the input images into features, and then analyze them independently. The result of this process feeds into a fully connected neural network structure which drives the final classification decision. In this paper, we propose a Kernelized Dense Layer (KDL) which captures higher order feature interactions instead of conventional linear relations.

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M.Amine Mahmoudi; Aladine Chetouani; Fatma Boufera; Hedi Tabia
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2 November 2020 - 4:37pm
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Kernelized Dense Layers For Facial Expression Recognition

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[1] M.Amine Mahmoudi; Aladine Chetouani; Fatma Boufera; Hedi Tabia, "Kernelized Dense Layers For Facial Expression Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5489. Accessed: Nov. 26, 2020.
@article{5489-20,
url = {http://sigport.org/5489},
author = {M.Amine Mahmoudi; Aladine Chetouani; Fatma Boufera; Hedi Tabia },
publisher = {IEEE SigPort},
title = {Kernelized Dense Layers For Facial Expression Recognition},
year = {2020} }
TY - EJOUR
T1 - Kernelized Dense Layers For Facial Expression Recognition
AU - M.Amine Mahmoudi; Aladine Chetouani; Fatma Boufera; Hedi Tabia
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5489
ER -
M.Amine Mahmoudi; Aladine Chetouani; Fatma Boufera; Hedi Tabia. (2020). Kernelized Dense Layers For Facial Expression Recognition. IEEE SigPort. http://sigport.org/5489
M.Amine Mahmoudi; Aladine Chetouani; Fatma Boufera; Hedi Tabia, 2020. Kernelized Dense Layers For Facial Expression Recognition. Available at: http://sigport.org/5489.
M.Amine Mahmoudi; Aladine Chetouani; Fatma Boufera; Hedi Tabia. (2020). "Kernelized Dense Layers For Facial Expression Recognition." Web.
1. M.Amine Mahmoudi; Aladine Chetouani; Fatma Boufera; Hedi Tabia. Kernelized Dense Layers For Facial Expression Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5489

CDVA/VCM: LANGUAGE FOR INTELLIGENT AND AUTONOMOUS VEHICLES


Intelligent transportation is a complex system that involves the interaction of connected technologies, including Smart Sensors, Intelligent and Autonomous Vehicles, High Precision Maps, and 5G. The coordination of all these machines mandates a common language that serves as a protocol for intelligent machines to communicate. International standards serve as the global protocol to satisfy industry needs at the product level. MPEG-CDVA is the official ISO standard for search and retrieval applications by providing Compact Descriptors for Video Analysis (CDVA).

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Authors:
Baohua Sun, Hao Sha, Manouchehr Rafie, Lin Yang
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2 November 2020 - 4:31pm
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[1] Baohua Sun, Hao Sha, Manouchehr Rafie, Lin Yang, "CDVA/VCM: LANGUAGE FOR INTELLIGENT AND AUTONOMOUS VEHICLES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5488. Accessed: Nov. 26, 2020.
@article{5488-20,
url = {http://sigport.org/5488},
author = {Baohua Sun; Hao Sha; Manouchehr Rafie; Lin Yang },
publisher = {IEEE SigPort},
title = {CDVA/VCM: LANGUAGE FOR INTELLIGENT AND AUTONOMOUS VEHICLES},
year = {2020} }
TY - EJOUR
T1 - CDVA/VCM: LANGUAGE FOR INTELLIGENT AND AUTONOMOUS VEHICLES
AU - Baohua Sun; Hao Sha; Manouchehr Rafie; Lin Yang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5488
ER -
Baohua Sun, Hao Sha, Manouchehr Rafie, Lin Yang. (2020). CDVA/VCM: LANGUAGE FOR INTELLIGENT AND AUTONOMOUS VEHICLES. IEEE SigPort. http://sigport.org/5488
Baohua Sun, Hao Sha, Manouchehr Rafie, Lin Yang, 2020. CDVA/VCM: LANGUAGE FOR INTELLIGENT AND AUTONOMOUS VEHICLES. Available at: http://sigport.org/5488.
Baohua Sun, Hao Sha, Manouchehr Rafie, Lin Yang. (2020). "CDVA/VCM: LANGUAGE FOR INTELLIGENT AND AUTONOMOUS VEHICLES." Web.
1. Baohua Sun, Hao Sha, Manouchehr Rafie, Lin Yang. CDVA/VCM: LANGUAGE FOR INTELLIGENT AND AUTONOMOUS VEHICLES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5488

ENCODING HIGH-THROUGHPUT JPEG2000 (HTJ2K) IMAGES ON A GPU


High-Throughput JPEG2000 (HTJ2K) is a new addition to the JPEG2000 suite of coding tools; it has been recently approved as Part-15 of the JPEG2000 standard, and the JPH file extension has been designated for it. The HTJ2K employs a new “fast” block coder that can achieve higher encoding and decoding throughput than a conventional JPEG2000 (C-J2K) encoder. The higher throughput is achieved because the HTJ2K codec processes wavelet coefficients in a smaller number of steps than C-J2K.

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2 November 2020 - 4:23pm
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[1] , "ENCODING HIGH-THROUGHPUT JPEG2000 (HTJ2K) IMAGES ON A GPU", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5487. Accessed: Nov. 26, 2020.
@article{5487-20,
url = {http://sigport.org/5487},
author = { },
publisher = {IEEE SigPort},
title = {ENCODING HIGH-THROUGHPUT JPEG2000 (HTJ2K) IMAGES ON A GPU},
year = {2020} }
TY - EJOUR
T1 - ENCODING HIGH-THROUGHPUT JPEG2000 (HTJ2K) IMAGES ON A GPU
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5487
ER -
. (2020). ENCODING HIGH-THROUGHPUT JPEG2000 (HTJ2K) IMAGES ON A GPU. IEEE SigPort. http://sigport.org/5487
, 2020. ENCODING HIGH-THROUGHPUT JPEG2000 (HTJ2K) IMAGES ON A GPU. Available at: http://sigport.org/5487.
. (2020). "ENCODING HIGH-THROUGHPUT JPEG2000 (HTJ2K) IMAGES ON A GPU." Web.
1. . ENCODING HIGH-THROUGHPUT JPEG2000 (HTJ2K) IMAGES ON A GPU [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5487

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