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Image/Video Coding

Low Complexity Joint Rate-Distortion Optimization of Prediction Units Couples for HEVC Intra Coding


HEVC is the latest block-based video compression standard, outperforming H.264/AVC by 50% bitrate savings for the same perceptual quality. An HEVC encoder provides Rate-Distortion optimization coding tools for block-wise compression. Because of complexity limitations, Rate-Distortion Optimization (RDO) is usually performed independently for each block, assuming coding efficiency losses to be negligible.

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Authors:
Maxime Bichon, Julien Le Tanou, Michael Ropert, Wassim Hamidouche, Luce Morin, Lu Zhang
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9 May 2018 - 4:09am
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mbichon_ICASSP18_poster_portrait.pdf

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[1] Maxime Bichon, Julien Le Tanou, Michael Ropert, Wassim Hamidouche, Luce Morin, Lu Zhang, "Low Complexity Joint Rate-Distortion Optimization of Prediction Units Couples for HEVC Intra Coding", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3206. Accessed: Jul. 17, 2018.
@article{3206-18,
url = {http://sigport.org/3206},
author = {Maxime Bichon; Julien Le Tanou; Michael Ropert; Wassim Hamidouche; Luce Morin; Lu Zhang },
publisher = {IEEE SigPort},
title = {Low Complexity Joint Rate-Distortion Optimization of Prediction Units Couples for HEVC Intra Coding},
year = {2018} }
TY - EJOUR
T1 - Low Complexity Joint Rate-Distortion Optimization of Prediction Units Couples for HEVC Intra Coding
AU - Maxime Bichon; Julien Le Tanou; Michael Ropert; Wassim Hamidouche; Luce Morin; Lu Zhang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3206
ER -
Maxime Bichon, Julien Le Tanou, Michael Ropert, Wassim Hamidouche, Luce Morin, Lu Zhang. (2018). Low Complexity Joint Rate-Distortion Optimization of Prediction Units Couples for HEVC Intra Coding. IEEE SigPort. http://sigport.org/3206
Maxime Bichon, Julien Le Tanou, Michael Ropert, Wassim Hamidouche, Luce Morin, Lu Zhang, 2018. Low Complexity Joint Rate-Distortion Optimization of Prediction Units Couples for HEVC Intra Coding. Available at: http://sigport.org/3206.
Maxime Bichon, Julien Le Tanou, Michael Ropert, Wassim Hamidouche, Luce Morin, Lu Zhang. (2018). "Low Complexity Joint Rate-Distortion Optimization of Prediction Units Couples for HEVC Intra Coding." Web.
1. Maxime Bichon, Julien Le Tanou, Michael Ropert, Wassim Hamidouche, Luce Morin, Lu Zhang. Low Complexity Joint Rate-Distortion Optimization of Prediction Units Couples for HEVC Intra Coding [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3206

Autoencoder-based image compression: can the learning be quantization independent?

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Authors:
Aline Roumy, Christine Guillemot
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20 April 2018 - 3:18pm
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presentation_icassp_2018.pdf

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[1] Aline Roumy, Christine Guillemot, "Autoencoder-based image compression: can the learning be quantization independent?", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3112. Accessed: Jul. 17, 2018.
@article{3112-18,
url = {http://sigport.org/3112},
author = {Aline Roumy; Christine Guillemot },
publisher = {IEEE SigPort},
title = {Autoencoder-based image compression: can the learning be quantization independent?},
year = {2018} }
TY - EJOUR
T1 - Autoencoder-based image compression: can the learning be quantization independent?
AU - Aline Roumy; Christine Guillemot
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3112
ER -
Aline Roumy, Christine Guillemot. (2018). Autoencoder-based image compression: can the learning be quantization independent?. IEEE SigPort. http://sigport.org/3112
Aline Roumy, Christine Guillemot, 2018. Autoencoder-based image compression: can the learning be quantization independent?. Available at: http://sigport.org/3112.
Aline Roumy, Christine Guillemot. (2018). "Autoencoder-based image compression: can the learning be quantization independent?." Web.
1. Aline Roumy, Christine Guillemot. Autoencoder-based image compression: can the learning be quantization independent? [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3112

LEARNING-BASED COMPLEXITY REDUCTION AND SCALING FOR HEVC ENCODERS

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Authors:
Mateus Grellert, Guilherme Correa, Sergio Bampi, Luis Cruz
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15 April 2018 - 12:44am
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ICASSP_Grellert_DecTrees.pdf

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[1] Mateus Grellert, Guilherme Correa, Sergio Bampi, Luis Cruz, "LEARNING-BASED COMPLEXITY REDUCTION AND SCALING FOR HEVC ENCODERS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2877. Accessed: Jul. 17, 2018.
@article{2877-18,
url = {http://sigport.org/2877},
author = {Mateus Grellert; Guilherme Correa; Sergio Bampi; Luis Cruz },
publisher = {IEEE SigPort},
title = {LEARNING-BASED COMPLEXITY REDUCTION AND SCALING FOR HEVC ENCODERS},
year = {2018} }
TY - EJOUR
T1 - LEARNING-BASED COMPLEXITY REDUCTION AND SCALING FOR HEVC ENCODERS
AU - Mateus Grellert; Guilherme Correa; Sergio Bampi; Luis Cruz
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2877
ER -
Mateus Grellert, Guilherme Correa, Sergio Bampi, Luis Cruz. (2018). LEARNING-BASED COMPLEXITY REDUCTION AND SCALING FOR HEVC ENCODERS. IEEE SigPort. http://sigport.org/2877
Mateus Grellert, Guilherme Correa, Sergio Bampi, Luis Cruz, 2018. LEARNING-BASED COMPLEXITY REDUCTION AND SCALING FOR HEVC ENCODERS. Available at: http://sigport.org/2877.
Mateus Grellert, Guilherme Correa, Sergio Bampi, Luis Cruz. (2018). "LEARNING-BASED COMPLEXITY REDUCTION AND SCALING FOR HEVC ENCODERS." Web.
1. Mateus Grellert, Guilherme Correa, Sergio Bampi, Luis Cruz. LEARNING-BASED COMPLEXITY REDUCTION AND SCALING FOR HEVC ENCODERS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2877

A JOINT SOURCE CHANNEL ARITHMETIC MAP DECODER USING PROBABILISTIC RELATIONS AMONG INTRA MODES IN PREDICTIVE VIDEO COMPRESSION


In this paper, residual redundancy in compressed videos is exploited to alleviate transmission errors using joint source channel arithmetic decoding. A new method is proposed to estimate a priori probability in MAP metric of H.264 intra modes decoder. The decoder generates a decoding tree using a breadth first search algorithm. An introduced statistical model is then implemented stage by stage over the decoding tree.

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Authors:
William E. Lynch, M. Omair Ahmad
Submitted On:
13 April 2018 - 4:42pm
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ICASSP_Presentation_20180409.pdf

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[1] William E. Lynch, M. Omair Ahmad, "A JOINT SOURCE CHANNEL ARITHMETIC MAP DECODER USING PROBABILISTIC RELATIONS AMONG INTRA MODES IN PREDICTIVE VIDEO COMPRESSION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2765. Accessed: Jul. 17, 2018.
@article{2765-18,
url = {http://sigport.org/2765},
author = {William E. Lynch; M. Omair Ahmad },
publisher = {IEEE SigPort},
title = {A JOINT SOURCE CHANNEL ARITHMETIC MAP DECODER USING PROBABILISTIC RELATIONS AMONG INTRA MODES IN PREDICTIVE VIDEO COMPRESSION},
year = {2018} }
TY - EJOUR
T1 - A JOINT SOURCE CHANNEL ARITHMETIC MAP DECODER USING PROBABILISTIC RELATIONS AMONG INTRA MODES IN PREDICTIVE VIDEO COMPRESSION
AU - William E. Lynch; M. Omair Ahmad
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2765
ER -
William E. Lynch, M. Omair Ahmad. (2018). A JOINT SOURCE CHANNEL ARITHMETIC MAP DECODER USING PROBABILISTIC RELATIONS AMONG INTRA MODES IN PREDICTIVE VIDEO COMPRESSION. IEEE SigPort. http://sigport.org/2765
William E. Lynch, M. Omair Ahmad, 2018. A JOINT SOURCE CHANNEL ARITHMETIC MAP DECODER USING PROBABILISTIC RELATIONS AMONG INTRA MODES IN PREDICTIVE VIDEO COMPRESSION. Available at: http://sigport.org/2765.
William E. Lynch, M. Omair Ahmad. (2018). "A JOINT SOURCE CHANNEL ARITHMETIC MAP DECODER USING PROBABILISTIC RELATIONS AMONG INTRA MODES IN PREDICTIVE VIDEO COMPRESSION." Web.
1. William E. Lynch, M. Omair Ahmad. A JOINT SOURCE CHANNEL ARITHMETIC MAP DECODER USING PROBABILISTIC RELATIONS AMONG INTRA MODES IN PREDICTIVE VIDEO COMPRESSION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2765

FAST 3D-HEVC DEPTH MAPS INTRA-FRAME PREDICTION USING DATA MINING


This paper presents a fast 3D-High Efficiency Video Coding (3D-HEVC) depth maps intra-frame prediction based on static Coding Unit (CU) splitting decisions trees. This coding approach uses data mining to extract the correlation among the encoder context attributes and to define a split decision tree for each CU level of the depth maps encoding. The decision trees were trained using the information extracted from 3D-HEVC Test Model (3D-HTM) and using the Common Test Conditions (CTC).

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Authors:
Mário Saldanha, Gustavo Sanchez, César Marcon, Luciano Agostini
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13 April 2018 - 2:54pm
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FAST 3D-HEVC DEPTH MAPS INTRA-FRAME PREDICTION USING DATA MINING.pdf

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[1] Mário Saldanha, Gustavo Sanchez, César Marcon, Luciano Agostini, "FAST 3D-HEVC DEPTH MAPS INTRA-FRAME PREDICTION USING DATA MINING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2746. Accessed: Jul. 17, 2018.
@article{2746-18,
url = {http://sigport.org/2746},
author = {Mário Saldanha; Gustavo Sanchez; César Marcon; Luciano Agostini },
publisher = {IEEE SigPort},
title = {FAST 3D-HEVC DEPTH MAPS INTRA-FRAME PREDICTION USING DATA MINING},
year = {2018} }
TY - EJOUR
T1 - FAST 3D-HEVC DEPTH MAPS INTRA-FRAME PREDICTION USING DATA MINING
AU - Mário Saldanha; Gustavo Sanchez; César Marcon; Luciano Agostini
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2746
ER -
Mário Saldanha, Gustavo Sanchez, César Marcon, Luciano Agostini. (2018). FAST 3D-HEVC DEPTH MAPS INTRA-FRAME PREDICTION USING DATA MINING. IEEE SigPort. http://sigport.org/2746
Mário Saldanha, Gustavo Sanchez, César Marcon, Luciano Agostini, 2018. FAST 3D-HEVC DEPTH MAPS INTRA-FRAME PREDICTION USING DATA MINING. Available at: http://sigport.org/2746.
Mário Saldanha, Gustavo Sanchez, César Marcon, Luciano Agostini. (2018). "FAST 3D-HEVC DEPTH MAPS INTRA-FRAME PREDICTION USING DATA MINING." Web.
1. Mário Saldanha, Gustavo Sanchez, César Marcon, Luciano Agostini. FAST 3D-HEVC DEPTH MAPS INTRA-FRAME PREDICTION USING DATA MINING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2746

CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM


Point cloud has attracted more and more attention in 3D object representation, especially in free-view rendering. However, it is challenging to efficiently deploy the point cloud due to its huge data amount with multiple attributes including coordinates, normal and color. In order to represent point clouds more compactly, we propose a novel point cloud compression method for attributes, based on geometric clustering and Normal Weighted Graph Fourier Transform (NWGFT).

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Authors:
Wei hu, Shanshe Wang, Xinfeng Zhang, Shiqi Wang,Siwei Ma,Wen Gao
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13 April 2018 - 4:56am
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ICASSP2018_poster_v4.pdf

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[1] Wei hu, Shanshe Wang, Xinfeng Zhang, Shiqi Wang,Siwei Ma,Wen Gao, "CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2661. Accessed: Jul. 17, 2018.
@article{2661-18,
url = {http://sigport.org/2661},
author = {Wei hu; Shanshe Wang; Xinfeng Zhang; Shiqi Wang;Siwei Ma;Wen Gao },
publisher = {IEEE SigPort},
title = {CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM},
year = {2018} }
TY - EJOUR
T1 - CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM
AU - Wei hu; Shanshe Wang; Xinfeng Zhang; Shiqi Wang;Siwei Ma;Wen Gao
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2661
ER -
Wei hu, Shanshe Wang, Xinfeng Zhang, Shiqi Wang,Siwei Ma,Wen Gao. (2018). CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM. IEEE SigPort. http://sigport.org/2661
Wei hu, Shanshe Wang, Xinfeng Zhang, Shiqi Wang,Siwei Ma,Wen Gao, 2018. CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM. Available at: http://sigport.org/2661.
Wei hu, Shanshe Wang, Xinfeng Zhang, Shiqi Wang,Siwei Ma,Wen Gao. (2018). "CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM." Web.
1. Wei hu, Shanshe Wang, Xinfeng Zhang, Shiqi Wang,Siwei Ma,Wen Gao. CLUSTER-BASED POINT CLOUD CODING WITH NORMAL WEIGHTED GRAPH FOURIER TRANSFORM [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2661

An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios


The compressed sensing (CS) has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been proposed and obtained superior performance. However, these methods suffer from blocking artifacts or ringing effects at low sampling ratios in most cases. To address this problem, we propose a deep convolutional Laplacian Pyramid Compressed Sensing Network (LapCSNet) for CS, which consists of a sampling sub-network and a reconstruction sub-network.

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Authors:
Wenxue Cui, Heyao Xu, Xinwei Gao, Shengping Zhang, Feng Jiang, Debin Zhao
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13 April 2018 - 1:16am
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Compressed sensing, Convolutional neural network, CNN

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[1] Wenxue Cui, Heyao Xu, Xinwei Gao, Shengping Zhang, Feng Jiang, Debin Zhao, "An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2600. Accessed: Jul. 17, 2018.
@article{2600-18,
url = {http://sigport.org/2600},
author = {Wenxue Cui; Heyao Xu; Xinwei Gao; Shengping Zhang; Feng Jiang; Debin Zhao },
publisher = {IEEE SigPort},
title = {An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios},
year = {2018} }
TY - EJOUR
T1 - An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios
AU - Wenxue Cui; Heyao Xu; Xinwei Gao; Shengping Zhang; Feng Jiang; Debin Zhao
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2600
ER -
Wenxue Cui, Heyao Xu, Xinwei Gao, Shengping Zhang, Feng Jiang, Debin Zhao. (2018). An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios. IEEE SigPort. http://sigport.org/2600
Wenxue Cui, Heyao Xu, Xinwei Gao, Shengping Zhang, Feng Jiang, Debin Zhao, 2018. An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios. Available at: http://sigport.org/2600.
Wenxue Cui, Heyao Xu, Xinwei Gao, Shengping Zhang, Feng Jiang, Debin Zhao. (2018). "An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios." Web.
1. Wenxue Cui, Heyao Xu, Xinwei Gao, Shengping Zhang, Feng Jiang, Debin Zhao. An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2600

OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION


Test Zone Search (TZS) is considered the current state-of-the-art fast Motion Estimation algorithm because it presents

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Authors:
Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa
Submitted On:
12 April 2018 - 12:44pm
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OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION.pdf

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[1] Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa, "OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2438. Accessed: Jul. 17, 2018.
@article{2438-18,
url = {http://sigport.org/2438},
author = {Marcelo Porto; Bruno Zatt; Luciano Agostini; Guilherme Correa },
publisher = {IEEE SigPort},
title = {OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION},
year = {2018} }
TY - EJOUR
T1 - OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION
AU - Marcelo Porto; Bruno Zatt; Luciano Agostini; Guilherme Correa
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2438
ER -
Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa. (2018). OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION. IEEE SigPort. http://sigport.org/2438
Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa, 2018. OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION. Available at: http://sigport.org/2438.
Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa. (2018). "OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION." Web.
1. Marcelo Porto, Bruno Zatt, Luciano Agostini, Guilherme Correa. OCTAGONAL-AXIS RASTER PATTERN FOR IMPROVED TEST ZONE SEARCH MOTION ESTIMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2438

FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC

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Authors:
Hamza Hamout, Abderrahmane Elyousfi
Submitted On:
12 April 2018 - 11:56am
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Poster_ICASSP_2018 (1).pdf

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[1] Hamza Hamout, Abderrahmane Elyousfi, "FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2422. Accessed: Jul. 17, 2018.
@article{2422-18,
url = {http://sigport.org/2422},
author = {Hamza Hamout; Abderrahmane Elyousfi },
publisher = {IEEE SigPort},
title = {FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC},
year = {2018} }
TY - EJOUR
T1 - FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC
AU - Hamza Hamout; Abderrahmane Elyousfi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2422
ER -
Hamza Hamout, Abderrahmane Elyousfi. (2018). FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC. IEEE SigPort. http://sigport.org/2422
Hamza Hamout, Abderrahmane Elyousfi, 2018. FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC. Available at: http://sigport.org/2422.
Hamza Hamout, Abderrahmane Elyousfi. (2018). "FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC." Web.
1. Hamza Hamout, Abderrahmane Elyousfi. FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2422

GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS


In this paper, we explore the use of graph-basedtransforms to capture correlation in light fields. We consider a scheme in which view synthesis is used as a first step to exploit inter-view correlation. Local graph-based transforms (GT) are then considered for energy compaction of the residue signals. The structure of the local graphs is derived from a coherent super-pixel over-segmentation of the different views. The GT is computed and applied in a separable manner with a first spatial unweighted transform followed by an inter-view GT.

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Authors:
Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot
Submitted On:
12 April 2018 - 11:48am
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[1] Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot, "GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2419. Accessed: Jul. 17, 2018.
@article{2419-18,
url = {http://sigport.org/2419},
author = {Mira Rizkallah; Xin Su; Thomas Maugey and Christine Guillemot },
publisher = {IEEE SigPort},
title = {GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS},
year = {2018} }
TY - EJOUR
T1 - GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS
AU - Mira Rizkallah; Xin Su; Thomas Maugey and Christine Guillemot
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2419
ER -
Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot. (2018). GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS. IEEE SigPort. http://sigport.org/2419
Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot, 2018. GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS. Available at: http://sigport.org/2419.
Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot. (2018). "GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS." Web.
1. Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot. GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2419

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