Clausen, C.[Clifford],
Wechsler, H.[Harry],
Color image compression using PCA and backpropagation learning,
PR(33), No. 9, September 2000, pp. 1555-1560.
Elsevier DOI
0005
BibRef
Zheng, M.,
Bu, J.,
Chen, C.A.,
Wang, C.,
Zhang, L.,
Qiu, G.,
Cai, D.,
Graph Regularized Sparse Coding for Image Representation,
IP(20), No. 5, May 2011, pp. 1327-1336.
IEEE DOI
1104
Find a basis set capturing high-level semantics in the data and learn
sparse coordinates in terms of the basis set.
BibRef
Zhang, C.,
He, X.,
Image Compression by Learning to Minimize the Total Error,
CirSysVideo(23), No. 4, April 2013, pp. 565-576.
IEEE DOI
1304
BibRef
Li, Y.[Yang],
Tao, X.M.[Xiao-Ming],
Lu, J.H.[Jian-Hua],
Hybrid model-and-object-based real-time conversational video coding,
SP:IC(35), No. 1, 2015, pp. 9-19.
Elsevier DOI
1506
Model-based video coding
BibRef
Xu, M.[Mai],
Li, S.X.[Sheng-Xi],
Lu, J.H.[Jian-Hua],
Zhu, W.[Wenwu],
Compressibility Constrained Sparse Representation With Learnt
Dictionary for Low Bit-Rate Image Compression,
CirSysVideo(24), No. 10, October 2014, pp. 1743-1757.
IEEE DOI
1411
BibRef
Earlier: A1, A3, A4, Only:
Sparse representation of texture patches for low bit-rate image
compression,
VCIP12(1-6).
IEEE DOI
1302
BibRef
Damerchilu, B.[Bahman],
Norouzzadeh, M.S.[Mohammad Sadegh],
Meybodi, M.R.[Mohammad Reza],
Motion estimation using learning automata,
MVA(27), No. 7, October 2016, pp. 1047-1061.
Springer DOI
1610
BibRef
Xu, M.[Mai],
Jiang, L.[Lai],
Ye, Z.T.[Zhao-Ting],
Wang, Z.[Zulin],
Bottom-up saliency detection with sparse representation of learnt
texture atoms,
PR(60), No. 1, 2016, pp. 348-360.
Elsevier DOI
1609
BibRef
Earlier: A2, A1, A3, A4:
Image Saliency Detection with Sparse Representation of Learnt Texture
Atoms,
RSL-CV15(894-902)
IEEE DOI
1602
Visual attention
Databases
BibRef
Xu, M.[Mai],
Jiang, L.[Lai],
Sun, X.,
Ye, Z.T.[Zhao-Ting],
Wang, Z.[Zulin],
Learning to Detect Video Saliency With HEVC Features,
IP(26), No. 1, January 2017, pp. 369-385.
IEEE DOI
1612
computer vision
BibRef
Sun, Y.P.[Yi-Peng],
Tao, X.M.[Xiao-Ming],
Li, Y.[Yang],
Lu, J.H.[Jian-Hua],
Dictionary Learning for Image Coding Based on Multisample Sparse
Representation,
CirSysVideo(24), No. 11, November 2014, pp. 2004-2010.
IEEE DOI
1411
compressed sensing
BibRef
Chen, Z.,
He, T.,
Jin, X.,
Wu, F.,
Learning for Video Compression,
CirSysVideo(30), No. 2, February 2020, pp. 566-576.
IEEE DOI
2002
Image coding, Video compression, Spatiotemporal phenomena,
Image reconstruction, Transform coding, Codecs, Video coding,
PixelMotionCNN
BibRef
Ma, L.[Lin],
Zhao, D.B.[De-Bin],
Gao, W.[Wen],
Learning-based image restoration for compressed images,
SP:IC(27), No. 1, January 2012, pp. 54-65.
Elsevier DOI
1201
Image restoration; Compression artifacts; Primitive
BibRef
Liu, X.M.[Xian-Ming],
Cheung, G.[Gene],
Wu, X.L.[Xiao-Lin],
Zhao, D.B.[De-Bin],
Random Walk Graph Laplacian-Based Smoothness Prior for Soft Decoding
of JPEG Images,
IP(26), No. 2, February 2017, pp. 509-524.
IEEE DOI
1702
BibRef
Earlier:
Inter-block consistent soft decoding of JPEG images with sparsity and
graph-signal smoothness priors,
ICIP15(1628-1632)
IEEE DOI
1512
Laplace equations.
graph signal processing; image decoding; sparse signal representation
BibRef
Liu, X.M.[Xian-Ming],
Wu, X.L.[Xiao-Lin],
Zhou, J.T.[Jian-Tao],
Zhao, D.B.[De-Bin],
Data-driven sparsity-based restoration of JPEG-compressed images in
dual transform-pixel domain,
CVPR15(5171-5178)
IEEE DOI
1510
BibRef
Liu, X.M.[Xian-Ming],
Wu, X.L.[Xiao-Lin],
Zhao, D.B.[De-Bin],
Sparsity-based soft decoding of compressed images in transform domain,
ICIP13(563-566)
IEEE DOI
1402
Decoding. Restore compressed images not decoded images.
BibRef
Yeh, C.H.[Chia-Hung],
Kang, L.W.[Li-Wei],
Chiou, Y.W.[Yi-Wen],
Lin, C.W.[Chia-Wen],
Jiang, S.J.F.[Shu-Jhen Fan],
Self-learning-based post-processing for image/video deblocking via
sparse representation,
JVCIR(25), No. 5, 2014, pp. 891-903.
Elsevier DOI
1406
BibRef
Earlier: A3, A1, A2, A4, A5:
Efficient image/video deblocking via sparse representation,
VCIP12(1-6).
IEEE DOI
1302
Blocking artifact
BibRef
Kang, L.W.,
Hsu, C.C.,
Zhuang, B.,
Lin, C.W.,
Yeh, C.H.,
Learning-Based Joint Super-Resolution and Deblocking for a Highly
Compressed Image,
MultMed(17), No. 7, July 2015, pp. 921-934.
IEEE DOI
1506
Dictionaries
BibRef
Zhang, B.C.[Bao-Chang],
Gu, J.X.[Jia-Xin],
Chen, C.[Chen],
Han, J.G.[Jun-Gong],
Su, X.B.[Xiang-Bo],
Cao, X.B.[Xian-Bin],
Liu, J.Z.[Jian-Zhuang],
One-two-one networks for compression artifacts reduction in remote
sensing,
PandRS(145), 2018, pp. 184-196.
Elsevier DOI
1810
Compression artifacts reduction, Remote sensing, Deep learning,
One-two-one network
BibRef
Gan, Z.L.[Zong-Liang],
Low Bit-Rate Compression Image Restoration through Subspace Joint
Regression Learning,
IEICE(E101-D), No. 10, October 2018, pp. 2539-2542.
WWW Link.
1810
BibRef
Wang, M.,
Xie, W.,
Meng, X.,
Zeng, H.,
Ngan, K.N.,
UHD Video Coding: A Light-Weight Learning-Based Fast Super-Block
Approach,
CirSysVideo(29), No. 10, October 2019, pp. 3083-3094.
IEEE DOI
1910
data compression, high definition video, image colour analysis,
image resolution, image texture, video coding, medium coding unit, HEVC
BibRef
Zhao, L.J.[Li-Jun],
Bai, H.H.[Hui-Hui],
Wang, A.H.[An-Hong],
Zhao, Y.[Yao],
Learning a virtual codec based on deep convolutional neural network
to compress image,
JVCIR(63), 2019, pp. 102589.
Elsevier DOI
1909
Image representation, Image compression, Soft-projection,
Virtual codec, Post-processing
BibRef
Yu, L.W.[Liang-Wei],
Shen, L.Q.[Li-Quan],
Yang, H.[Hao],
Wang, L.[Lu],
An, P.[Ping],
Quality Enhancement Network via Multi-Reconstruction Recursive
Residual Learning for Video Coding,
SPLetters(26), No. 4, April 2019, pp. 557-561.
IEEE DOI
1903
Image reconstruction, Feature extraction, Training, Image coding,
Encoding, Periodic structures, Compression algorithms,
quality enhancement
BibRef
Liu, Y.[Ying],
Tountas, K.[Konstantinos],
Pados, D.A.[Dimitris A.],
Batalama, S.N.[Stella N.],
Medley, M.J.[Michael J.],
L1-Subspace Tracking for Streaming Data,
PR(97), 2020, pp. 106992.
Elsevier DOI
1910
Dimensionality reduction, Eigenvector decomposition,
Internet-of-Things, -norm, Outliers, Subspace learning
BibRef
Fu, H.S.[Hai-Sheng],
Liang, F.[Feng],
Lei, B.[Bo],
Bian, N.[Nai],
Zhang, Q.[Qian],
Akbari, M.[Mohammad],
Liang, J.[Jie],
Tu, C.J.[Cheng-Jie],
Improved hybrid layered image compression using deep learning and
traditional codecs,
SP:IC(82), 2020, pp. 115774.
Elsevier DOI
2001
Deep learning-based image coding, Layered image coding,
Residual coding, Convolutional neural network, Autoencoder
BibRef
Song, Q.,
Xiong, R.,
Fan, X.,
Liu, D.,
Wu, F.,
Huang, T.,
Gao, W.,
Compressed Image Restoration via Artifacts-Free PCA Basis Learning
and Adaptive Sparse Modeling,
IP(29), 2020, pp. 7399-7413.
IEEE DOI
2007
Compressed image restoration, sparse modeling,
paired PCA learning, adaptive distribution modeling
BibRef
Liu, J.,
Liu, D.,
Yang, W.,
Xia, S.,
Zhang, X.,
Dai, Y.,
A Comprehensive Benchmark for Single Image Compression Artifact
Reduction,
IP(29), 2020, pp. 7845-7860.
IEEE DOI
2007
Compression artifacts removal, benchmark, side information,
loop filter, deep learning
BibRef
Cheng, Z.,
Sun, H.,
Takeuchi, M.,
Katto, J.,
Energy Compaction-Based Image Compression Using Convolutional
AutoEncoder,
MultMed(22), No. 4, April 2020, pp. 860-873.
IEEE DOI
2004
Image compression, convolutional autoencoder,
optimum bit allocation, energy compaction
BibRef
Yeo, Y.,
Shin, Y.,
Sagong, M.,
Kim, S.,
Ko, S.,
Simple Yet Effective Way for Improving the Performance of Lossy Image
Compression,
SPLetters(27), 2020, pp. 530-534.
IEEE DOI
2005
Convolutional neural network, deep learning, image compression
BibRef
Cai, J.,
Cao, Z.,
Zhang, L.,
Learning a Single Tucker Decomposition Network for Lossy Image
Compression With Multiple Bits-per-Pixel Rates,
IP(29), 2020, pp. 3612-3625.
IEEE DOI
2002
Lossy image compression, convolutional neural networks, tucker decomposition
BibRef
Schiopu, I.,
Munteanu, A.,
Deep-Learning-Based Lossless Image Coding,
CirSysVideo(30), No. 7, July 2020, pp. 1829-1842.
IEEE DOI
2007
Image coding, Cameras, Context modeling, Tools, Codecs,
Prediction methods, Standards, Machine learning, image coding, context modeling
BibRef
Liao, L.[Liang],
Xiao, J.[Jing],
Li, Y.T.[Ya-Ting],
Wang, M.[Mi],
Hu, R.M.[Rui-Min],
Learned Representation of Satellite Image Series for Data Compression,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link
2002
Long term background redundancy.
BibRef
Klopp, J.P.[Jan P.],
Chen, L.G.[Liang-Gee],
Chien, S.Y.[Shao-Yi],
Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video
Coding,
IP(29), 2020, pp. 6372-6385.
IEEE DOI
2006
Image coding, Redundancy, Decoding, Neural networks,
Complexity theory, Video codecs, Noise reduction, Video coding,
machine learning
BibRef
Paul, S.,
Norkin, A.,
Bovik, A.C.,
Speeding Up VP9 Intra Encoder With Hierarchical Deep Learning-Based
Partition Prediction,
IP(29), 2020, pp. 8134-8148.
IEEE DOI
2008
Machine learning, Image coding, Databases, Encoding, Video coding,
Task analysis, Video codecs, VP9, video encoding, block partitioning,
machine learning
BibRef
Chen, L.H.,
Bampis, C.G.,
Li, Z.,
Norkin, A.,
Bovik, A.C.,
ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image
Compression,
IP(30), 2021, pp. 360-373.
IEEE DOI
2012
Image coding, Optimization, Image quality, Training, Task analysis,
Indexes, Loss measurement, Perceptual optimization,
deep compression
BibRef
Huang, C.[Chao],
Peng, Z.J.[Zong-Ju],
Xu, Y.[Yong],
Chen, F.[Fen],
Jiang, Q.P.[Qiu-Ping],
Zhang, Y.[Yun],
Jiang, G.Y.[Gang-Yi],
Ho, Y.S.[Yo-Sung],
Online Learning-Based Multi-Stage Complexity Control for Live Video
Coding,
IP(30), 2021, pp. 641-656.
IEEE DOI
2012
Complexity allocation, complexity control,
high efficiency video coding, online learning, random forest
BibRef
Chen, T.,
Liu, H.,
Ma, Z.,
Shen, Q.,
Cao, X.,
Wang, Y.,
End-to-End Learnt Image Compression via Non-Local Attention
Optimization and Improved Context Modeling,
IP(30), 2021, pp. 3179-3191.
IEEE DOI
2103
Image coding, Context modeling,
Transforms, Transform coding, Correlation, Complexity theory,
variable-rate model
BibRef
Kudo, S.[Shinobu],
Orihashi, S.[Shota],
Tanida, R.[Ryuichi],
Takamura, S.[Seishi],
Kimata, H.[Hideaki],
GAN-Based Image Compression Using Mutual Information for Optimizing
Subjective Image Similarity,
IEICE(E104-D), No. 3, March 2021, pp. 450-460.
WWW Link.
2103
BibRef
Wang, J.,
Duan, Y.,
Tao, X.,
Xu, M.,
Lu, J.,
Semantic Perceptual Image Compression With a Laplacian Pyramid of
Convolutional Networks,
IP(30), 2021, pp. 4225-4237.
IEEE DOI
2104
BibRef
Earlier: A1, A3, A4, A5, Only:
ICIP19(699-703)
IEEE DOI
1910
Image coding, Laplace equations, Semantics,
Generative adversarial networks, Training, Image reconstruction,
perceptual quality.
image compression, deep learning, Laplacian pyramid,
adversarial network, perceptual loss
BibRef
Ding, D.D.[Dan-Dan],
Kong, L.[Lingyi],
Wang, W.Y.[Wen-Yu],
Zhu, F.Q.[Feng-Qing],
A progressive CNN in-loop filtering approach for inter frame coding,
SP:IC(94), 2021, pp. 116201.
Elsevier DOI
2104
CNN, In-loop filtering, Model training, Inter coding
BibRef
Dua, Y.[Yaman],
Singh, R.S.[Ravi Shankar],
Parwani, K.[Kshitij],
Lunagariya, S.[Smit],
Kumar, V.[Vinod],
Convolution Neural Network based lossy compression of hyperspectral
images,
SP:IC(95), 2021, pp. 116255.
Elsevier DOI
2106
Autoencoder, Lossy compression, Convolution Neural Network,
Hyperspectral images, Learning based compression
BibRef
Zhao, S.H.[Shi-Hui],
Yang, S.Y.[Shu-Yuan],
Gu, J.[Jing],
Liu, Z.[Zhi],
Feng, Z.X.[Zhi-Xi],
Symmetrical lattice generative adversarial network for remote sensing
images compression,
PandRS(176), 2021, pp. 169-181.
Elsevier DOI
2106
Generative adversarial network,
Remote sensing image compression, Symmetrical lattice, Cooperative learning
BibRef
Chen, Y.H.[Yi-Hao],
Tan, B.[Bin],
Wu, J.[Jun],
Zhang, Z.F.[Zhi-Feng],
Ren, H.Q.[Hao-Qi],
A Deep Image Coding Scheme With Generative Network to Learn From
Correlated Images,
MultMed(23), 2021, pp. 2235-2244.
IEEE DOI
2108
Image coding, Image reconstruction, Inverse problems,
Generative adversarial networks, Generators, Deep learning, inverse problem
BibRef
Liu, H.J.[Hao-Jie],
Lu, M.[Ming],
Ma, Z.[Zhan],
Wang, F.[Fan],
Xie, Z.H.[Zhi-Huang],
Cao, X.[Xun],
Wang, Y.[Yao],
Neural Video Coding Using Multiscale Motion Compensation and
Spatiotemporal Context Model,
CirSysVideo(31), No. 8, August 2021, pp. 3182-3196.
IEEE DOI
2108
Video coding, Motion compensation, Encoding, Bit rate, Decoding,
Image coding, Spatiotemporal phenomena, Neural video coding,
temporal error propagation
BibRef
Ding, D.D.[Dan-Dan],
Ma, Z.[Zhan],
Chen, D.[Di],
Chen, Q.S.[Qing-Shuang],
Liu, Z.[Zoe],
Zhu, F.Q.[Feng-Qing],
Advances in Video Compression System Using Deep Neural Network:
A Review and Case Studies,
PIEEE(109), No. 9, September 2021, pp. 1494-1520.
IEEE DOI
2108
Encoding, Video compression, Video coding, Streaming media,
Visualization, Quality of experience, Spatiotemporal phenomena,
texture analysis
BibRef
Chen, T.[Tong],
Liu, H.J.[Hao-Jie],
Shen, Q.[Qiu],
Yue, T.[Tao],
Cao, X.[Xun],
Ma, Z.[Zhan],
DeepCoder: A deep neural network based video compression,
VCIP17(1-4)
IEEE DOI
1804
Huffman codes, convolution, data compression,
feedforward neural nets, video coding,
video compression
BibRef
Wang, Z.X.[Zi-Xi],
Ding, G.G.[Gui-Guang],
Han, J.G.[Jun-Gong],
Li, F.[Fan],
Deep image compression with multi-stage representation,
JVCIR(79), 2021, pp. 103226.
Elsevier DOI
2109
Deep image compression, Multi-stage representation,
Data-dependent probability model, Convolutional neural network
BibRef
Lu, G.[Guo],
Zhang, X.Y.[Xiao-Yun],
Ouyang, W.L.[Wan-Li],
Chen, L.[Li],
Gao, Z.Y.[Zhi-Yong],
Xu, D.[Dong],
An End-to-End Learning Framework for Video Compression,
PAMI(43), No. 10, October 2021, pp. 3292-3308.
IEEE DOI
2109
Image coding, Video compression, Optical imaging,
Motion estimation, Optical distortion, Estimation, Adaptive optics,
image compression
BibRef
Lu, G.[Guo],
Ouyang, W.L.[Wan-Li],
Xu, D.[Dong],
Zhang, X.Y.[Xiao-Yun],
Cai, C.L.[Chun-Lei],
Gao, Z.Y.[Zhi-Yong],
DVC: An End-To-End Deep Video Compression Framework,
CVPR19(10998-11007).
IEEE DOI
2002
BibRef
Hong, W.X.[Wei-Xin],
Chen, T.[Tong],
Lu, M.[Ming],
Pu, S.L.[Shi-Liang],
Ma, Z.[Zhan],
Efficient Neural Image Decoding via Fixed-Point Inference,
CirSysVideo(31), No. 9, September 2021, pp. 3618-3630.
IEEE DOI
2109
Image coding, Decoding, Task analysis, Convolutional codes,
Transform coding, Quantization (signal), Dynamic range,
range-dependent normalization
BibRef
Sun, S.[Simeng],
He, T.[Tianyu],
Chen, Z.B.[Zhi-Bo],
Semantic Structured Image Coding Framework for Multiple Intelligent
Applications,
CirSysVideo(31), No. 9, September 2021, pp. 3631-3642.
IEEE DOI
2109
Image coding, Task analysis, Bit rate, Image reconstruction,
Amplitude modulation, Semantics, Computational modeling,
neural networks
BibRef
Akbari, M.[Mohammad],
Liang, J.[Jie],
Han, J.[Jingning],
Tu, C.J.[Cheng-Jie],
Learned Multi-Resolution Variable-Rate Image Compression With
Octave-Based Residual Blocks,
MultMed(23), 2021, pp. 3013-3021.
IEEE DOI
2109
Image coding, Decoding, Convolutional codes, Transforms, Codecs,
Image reconstruction, Linear programming, Deep learning, variable-rate
BibRef
Zhang, F.[Fan],
Ma, D.[Di],
Feng, C.[Chen],
Bull, D.R.[David R.],
Video Compression With CNN-Based Postprocessing,
MultMedMag(28), No. 4, October 2021, pp. 74-83.
IEEE DOI
2112
Encoding, Training, Generators, Image coding, Convolutional codes,
Video compression, Tools
BibRef
Karaca, A.C.[Ali Can],
Kara, O.[Ozan],
Güllü, M.K.[Mehmet Kemal],
MultiTempGAN: Multitemporal multispectral image compression framework
using generative adversarial networks,
JVCIR(81), 2021, pp. 103385.
Elsevier DOI
2112
Multispectral image compression,
Generative adversarial networks, Big data, Remote sensing,
Multitemporal images
BibRef
Ding, D.D.[Dan-Dan],
Gao, X.[Xiang],
Tang, C.[Chenran],
Ma, Z.[Zhan],
Neural Reference Synthesis for Inter Frame Coding,
IP(31), 2022, pp. 773-787.
IEEE DOI
2201
Image coding, Image reconstruction, Training, Encoding,
Quantization (signal), Correlation,
neural reference synthesis
BibRef
Chen, M.X.[Mei-Xu],
Goodall, T.[Todd],
Patney, A.[Anjul],
Bovik, A.C.[Alan C.],
Learning to compress videos without computing motion,
SP:IC(103), 2022, pp. 116633.
Elsevier DOI
2203
Video compression, Deep learning, Motion
BibRef
Guo, Z.Y.[Zong-Yu],
Zhang, Z.Z.[Zhi-Zheng],
Feng, R.S.[Run-Sen],
Chen, Z.B.[Zhi-Bo],
Causal Contextual Prediction for Learned Image Compression,
CirSysVideo(32), No. 4, April 2022, pp. 2329-2341.
IEEE DOI
2204
Context modeling, Predictive models, Image coding, Entropy,
Correlation, Entropy coding, Transforms, Learned image compression,
improved entropy model
BibRef
Liu, H.J.[Hao-Jie],
Lu, M.[Ming],
Chen, Z.[Zhiqi],
Cao, X.[Xun],
Ma, Z.[Zhan],
Wang, Y.[Yao],
End-to-End Neural Video Coding Using a Compound Spatiotemporal
Representation,
CirSysVideo(32), No. 8, August 2022, pp. 5650-5662.
IEEE DOI
2208
Image coding, Spatiotemporal phenomena, Decoding,
Chemical reactors, Video coding, Feature extraction, Optical flow,
video prediction
BibRef
Ma, D.[Di],
Zhang, F.[Fan],
Bull, D.R.[David R.],
BVI-DVC: A Training Database for Deep Video Compression,
MultMed(24), 2022, pp. 3847-3858.
IEEE DOI
2208
Databases, Training, Spatial resolution, Encoding, Spatial databases,
Standards, Video compression, Video database, BVI-DVC, CNN training,
video compression
BibRef
Mei, Y.X.[Yi-Xin],
Li, L.[Li],
Li, Z.[Zhu],
Li, F.[Fan],
Learning-Based Scalable Image Compression With Latent-Feature Reuse
and Prediction,
MultMed(24), 2022, pp. 4143-4157.
IEEE DOI
2208
Image coding, Standards, Redundancy, Streaming media, Scalability,
Image reconstruction, Spatial resolution,
convolutional neural network
BibRef
Li, D.W.[Dao-Wen],
Li, Y.M.[Ying-Ming],
Sun, H.M.[He-Ming],
Yu, L.[Lu],
Deep image compression based on multi-scale deformable convolution,
JVCIR(87), 2022, pp. 103573.
Elsevier DOI
2208
Deep image compression, Multi-scale deformable convolution, Spatial attention
BibRef
Lopes, L.S.[Lucas S.],
Chou, P.A.[Philip A.],
de Queiroz, R.L.[Ricardo L.],
Adaptive Context Modeling for Arithmetic Coding Using Perceptrons,
SPLetters(29), 2022, pp. 2382-2386.
IEEE DOI
2212
Context modeling, Training, Adaptation models, Table lookup, Symbols,
Encoding, Codes, Adaptive arithmetic coding, neural context modeling
BibRef
Tang, Z.[Zhisen],
Wang, H.[Hanli],
Yi, X.K.[Xiao-Kai],
Zhang, Y.[Yun],
Kwong, S.[Sam],
Kuo, C.C.J.[C.C. Jay],
Joint Graph Attention and Asymmetric Convolutional Neural Network for
Deep Image Compression,
CirSysVideo(33), No. 1, January 2023, pp. 421-433.
IEEE DOI
2301
Image coding, Convolution, Convolutional neural networks, Training,
Kernel, Visualization, Rate-distortion, Image compression,
variational autoencoder
BibRef
Ji, R.[Ruolei],
Karam, L.J.[Lina J.],
Learning-based Visual Compression,
FTCGV(15), No. 1, 2023, pp. 1-112.
DOI Link
2302
Survey, Compression.
BibRef
Tsubota, K.[Koki],
Akutsu, H.[Hiroaki],
Aizawa, K.[Kiyoharu],
Universal Deep Image Compression via Content-Adaptive Optimization
with Adapters,
WACV23(2528-2537)
IEEE DOI
2302
Adaptation models, Image coding, Codes, Transform coding,
Rate-distortion, Benchmark testing,
image and video synthesis
BibRef
Brummer, B.[Benoit],
de Vleeschouwer, C.[Christophe],
On the Importance of Denoising when Learning to Compress Images,
WACV23(2439-2447)
IEEE DOI
2302
Training, Adaptation models, Image coding, Noise reduction,
Rate-distortion, Transform coding, Noise measurement
BibRef
Li, M.[Meng],
Gao, S.Y.[Shang-Yin],
Feng, Y.[Yihui],
Shi, Y.[Yibo],
Wang, J.[Jing],
Content-Oriented Learned Image Compression,
ECCV22(XIX:632-647).
Springer DOI
2211
BibRef
Shi, Y.[Yibo],
Ge, Y.Y.[Yun-Ying],
Wang, J.[Jing],
Mao, J.[Jue],
AlphaVC: High-Performance and Efficient Learned Video Compression,
ECCV22(XIX:616-631).
Springer DOI
2211
BibRef
Wang, F.[Feng],
Chen, J.Y.[Jing-Yi],
Wang, R.G.[Rong-Gang],
Entropy-Reduced Attention for Image Compression,
ICIP22(2401-2405)
IEEE DOI
2211
Deep learning, Image coding, Uncertainty, Redundancy, Entropy,
Entropy coding, Decoding, Deep Learning, Image Compression,
Attention Mechanism
BibRef
Brand, F.[Fabian],
Seiler, J.[Jürgen],
Kaup, A.[André],
P-Frame Coding with Generalized Difference:
A Novel Conditional Coding Approach,
ICIP22(1266-1270)
IEEE DOI
2211
Image coding, Redundancy, Transform coding, Focusing, Switches,
Video compression, Complexity theory, Video Compression,
Deep Learning
BibRef
Koyuncu, A.B.[A. Burakhan],
Gao, H.[Han],
Boev, A.[Atanas],
Gaikov, G.[Georgii],
Alshina, E.[Elena],
Steinbach, E.[Eckehard],
Contextformer: A Transformer with Spatio-Channel Attention for Context
Modeling in Learned Image Compression,
ECCV22(XIX:447-463).
Springer DOI
2211
BibRef
Cheng, K.L.[Ka Leong],
Xie, Y.[Yueqi],
Chen, Q.F.[Qi-Feng],
Optimizing Image Compression via Joint Learning with Denoising,
ECCV22(XIX:56-73).
Springer DOI
2211
BibRef
Liu, C.[Chao],
Sun, H.M.[He-Ming],
Zeng, X.Y.[Xiao-Yang],
Fan, Y.[Yibo],
Learned Video Compression With Residual Prediction And Feature-Aided
Loop Filter,
ICIP22(1321-1325)
IEEE DOI
2211
Image coding, Redundancy, Video compression, Predictive models,
Motion compensation, Complexity theory, Decoding, residual prediction
BibRef
Liu, Z.Y.[Zi-Yi],
Wang, H.[Hanli],
Su, T.[Taiyi],
Learned Image Compression with Multi-Scale Spatial and Contextual
Information Fusion,
ICIP22(706-710)
IEEE DOI
2211
Video coding, Visualization, Solid modeling, Image coding,
Convolution, Neural networks, Image compression, deep learning,
multi-scale 3D context
BibRef
Zhang, Z.B.[Zhao-Bin],
Li, Y.[Yue],
Zhang, K.[Kai],
Zhang, L.[Li],
He, Y.[Yuwen],
Learning-Based End-to-End Video Compression with Spatial-Temporal
Adaptation,
ICIP22(2821-2825)
IEEE DOI
2211
Interpolation, Image coding, Correlation, Redundancy,
Rate-distortion, Switches, Video compression, frame extrapolation
BibRef
Zhang, G.[Gai],
Zhang, X.F.[Xin-Feng],
Zhu, S.Y.[Shu-Yuan],
Local and Global Fusion Network for Learned Image Compression,
ICIP22(3763-3767)
IEEE DOI
2211
Video coding, Image coding, Convolution, Fuses, Neural networks,
Redundancy, Information retrieval, Image compression, autoencoder
BibRef
Pan, G.B.[Guan-Bo],
Lu, G.[Guo],
Hu, Z.H.[Zhi-Hao],
Xu, D.[Dong],
Content Adaptive Latents and Decoder for Neural Image Compression,
ECCV22(XVIII:556-573).
Springer DOI
2211
Neural compression, usually not adaptive.
BibRef
Said, A.[Amir],
Pourreza, R.[Reza],
Le, H.[Hoang],
Optimized Learned Entropy Coding Parameters for Practical
Neural-Based Image and Video Compression,
ICIP22(661-665)
IEEE DOI
2211
Quantization (signal), Image coding, Codes, Redundancy,
Memory management, Video compression, Minimization, entropy coding
BibRef
Lin, F.Z.[Fang-Zheng],
Sun, H.M.[He-Ming],
Katto, J.[Jiro],
Streaming-Capable High-Performance Architecture of Learned Image
Compression Codecs,
ICIP22(286-290)
IEEE DOI
2211
Performance evaluation, Image coding, Runtime, Codecs,
Computational modeling, Streaming media,
pipelining
BibRef
Wang, H.R.[Huai-Rui],
Ren, G.J.[Guang-Jie],
Ouyang, T.[Tong],
Zhang, J.X.[Jun-Xi],
Han, W.W.[Wen-Wei],
Liu, Z.Z.[Zi-Zheng],
Chen, Z.Z.[Zhen-Zhong],
Perceptual in-Loop Filter for Image and Video Compression,
CLIC22(1769-1772)
IEEE DOI
2210
Visualization, Image coding, Focusing,
Video compression, Generative adversarial networks
BibRef
Lu, G.[Guo],
Zhong, T.X.[Tian-Xiong],
Geng, J.[Jing],
Hu, Q.[Qiang],
Xu, D.[Dong],
Learning based Multi-modality Image and Video Compression,
CVPR22(6073-6082)
IEEE DOI
2210
Visualization, Image coding, Correlation, Redundancy, Transforms,
Video compression, Low-level vision, 3D from multi-view and sensors
BibRef
He, D.[Dailan],
Yang, Z.M.[Zi-Ming],
Peng, W.[Weikun],
Ma, R.[Rui],
Qin, H.W.[Hong-Wei],
Wang, Y.[Yan],
ELIC: Efficient Learned Image Compression with Unevenly Grouped
Space-Channel Contextual Adaptive Coding,
CVPR22(5708-5717)
IEEE DOI
2210
Adaptation models, Image coding, Computational modeling,
Transforms, Decoding, Low-level vision
BibRef
He, D.[Dailan],
Yang, Z.M.[Zi-Ming],
Yu, H.J.[Hong-Jiu],
Xu, T.[Tongda],
Luo, J.X.[Ji-Xiang],
Chen, Y.[Yuan],
Gao, C.J.[Chen-Jian],
Shi, X.[Xinjie],
Qin, H.W.[Hong-Wei],
Wang, Y.[Yan],
PO-ELIC: Perception-Oriented Efficient Learned Image Coding,
CLIC22(1763-1768)
IEEE DOI
2210
Measurement, Training, Image quality, Adaptation models,
Visualization, Image coding, Image color analysis
BibRef
Sigger, N.[Neetu],
Al-Jawed, N.[Naseer],
Nguyen, T.[Tuan],
Spatial-Temporal Autoencoder with Attention Network for Video
Compression,
CIAP22(III:290-300).
Springer DOI
2205
BibRef
Gao, G.[Ge],
You, P.[Pei],
Pan, R.[Rong],
Han, S.Y.[Shun-Yuan],
Zhang, Y.Y.[Yuan-Yuan],
Dai, Y.C.[Yu-Chao],
Lee, H.[Hojae],
Neural Image Compression via Attentional Multi-scale Back Projection
and Frequency Decomposition,
ICCV21(14657-14666)
IEEE DOI
2203
Training, Video coding, Image coding, Estimation, Standards,
Next generation networking, Image and video synthesis,
Vision applications and systems
BibRef
Liu, J.M.[Jia-Ming],
Lu, M.[Ming],
Chen, K.X.[Kai-Xin],
Li, X.Q.[Xiao-Qi],
Wang, S.Z.[Shi-Zun],
Wang, Z.Q.[Zhao-Qing],
Wu, E.[Enhua],
Chen, Y.[Yurong],
Zhang, C.[Chuang],
Wu, M.[Ming],
Overfitting the Data:
Compact Neural Video Delivery via Content-aware Feature Modulation,
ICCV21(4611-4620)
IEEE DOI
2203
Training, Video coding, Neural networks, Modulation, Streaming media,
Explosions, Internet, Low-level and physics-based vision,
Vision applications and systems
BibRef
Fischer, K.[Kristian],
Forsch, C.[Christian],
Herglotz, C.[Christian],
Kaup, A.[André],
Analysis of Neural Image Compression Networks for Machine-To-Machine
Communication,
ICIP21(2079-2083)
IEEE DOI
2201
Training, Weight measurement, Video coding,
Machine-to-machine communications, Image coding, Codecs
BibRef
Haase, P.[Paul],
Becking, D.[Daniel],
Kirchhoffer, H.[Heiner],
Müller, K.[Karsten],
Schwarz, H.[Heiko],
Samek, W.[Wojciech],
Marpe, D.[Detlev],
Wiegand, T.[Thomas],
Encoder Optimizations for the NNR Standard on Neural Network
Compression,
ICIP21(3522-3526)
IEEE DOI
2201
Image coding, Quantization (signal), Tensors, Transform coding,
Optimization methods, Artificial neural networks, Tools, MPEG, NNR,
encoder optimization
BibRef
Yílmaz, M.A.[M. Akín],
Keless, O.[Onur],
Güven, H.[Hilal],
Tekalp, A.M.[A. Murat],
Malik, J.[Junaid],
Kíranyaz, S.[Serkan],
Self-Organized Variational Autoencoders (Self-Vae) for Learned Image
Compression,
ICIP21(3732-3736)
IEEE DOI
2201
Convolutional codes, Measurement, Visualization, Image coding,
Codecs, Neurons, Rate-distortion, perceptual quality metrics
BibRef
Mikami, Y.[Yu],
Tsutake, C.[Chihiro],
Takahashi, K.[Keita],
Fujii, T.[Toshiaki],
An Efficient Image Compression Method Based on Neural Network:
An Overfitting Approach,
ICIP21(2084-2088)
IEEE DOI
2201
Image quality, Visualization, Image coding, Quantization (signal),
Image edge detection, Rate-distortion,
parameter visualization
BibRef
Tsubota, K.[Koki],
Aizawa, K.[Kiyoharu],
Comprehensive Comparisons of Uniform Quantizers for Deep Image
Compression,
ICIP21(2089-2093)
IEEE DOI
2201
Quantization (signal), Image coding, Rate-distortion, Focusing,
Entropy, Entropy coding, Image Compression, Neural Networks, Quantization
BibRef
Yang, C.H.[Chun-Hui],
Ma, Y.[Yi],
Yang, J.Y.[Jia-Yu],
Liu, S.Y.[Shi-Yi],
Wang, R.G.[Rong-Gang],
Graph-Convolution Network for Image Compression,
ICIP21(2094-2098)
IEEE DOI
2201
Image coding, Quantization (signal), Convolution, Neural networks,
Feature extraction, Entropy, Image compression, graph convolution,
deep learning
BibRef
Yuan, L.[Liang],
Luo, J.X.[Ji-Xiang],
Li, S.H.[Shao-Hui],
Dai, W.[Wenrui],
Li, C.[Chenglin],
Zou, J.[Junni],
Xiong, H.K.[Hong-Kai],
Learned Image Compression with Channel-Wise Grouped Context Modeling,
ICIP21(2099-2103)
IEEE DOI
2201
Deep learning, Solid modeling, Image coding, Correlation, Redundancy,
Rate-distortion, Entropy coding, Image compression,
coding efficiency
BibRef
Dick, J.[João],
Abreu, B.[Brunno],
Grellert, M.[Mateus],
Bampi, S.[Sergio],
Quality and Complexity Assessment of Learning-Based Image Compression
Solutions,
ICIP21(599-603)
IEEE DOI
2201
Measurement, Visualization, Image coding, Codecs, Bit rate,
Transform coding, image compression, learning-based
BibRef
He, D.[Dailan],
Zheng, Y.Y.[Yao-Yan],
Sun, B.C.[Bao-Cheng],
Wang, Y.[Yan],
Qin, H.W.[Hong-Wei],
Checkerboard Context Model for Efficient Learned Image Compression,
CVPR21(14766-14775)
IEEE DOI
2111
Image coding, Computational modeling, Redundancy,
Rate-distortion, Decoding, Pattern recognition
BibRef
Liu, Y.C.[Yu-Chen],
Shu, Z.X.[Zhi-Xin],
Li, Y.J.[Yi-Jun],
Lin, Z.[Zhe],
Perazzi, F.[Federico],
Kung, S.Y.,
Content-Aware GAN Compression,
CVPR21(12151-12161)
IEEE DOI
2111
Manifolds, Image quality, Visualization, Image coding,
Image synthesis, Pipelines, Generative adversarial networks
BibRef
Weber, M.[Maurice],
Renggli, C.[Cedric],
Grabner, H.[Helmut],
Zhang, C.[Ce],
Observer Dependent Lossy Image Compression,
GCPR20(130-144).
Springer DOI
2110
BibRef
Ho, Y.H.[Yung-Han],
Chang, C.P.[Chih-Peng],
Chen, P.Y.[Peng-Yu],
Gnutti, A.[Alessandro],
Peng, W.H.[Wen-Hsiao],
CANF-VC: Conditional Augmented Normalizing Flows for Video Compression,
ECCV22(XVI:207-223).
Springer DOI
2211
BibRef
Ho, Y.H.[Yung-Han],
Chan, C.C.[Chih-Chun],
Peng, W.H.[Wen-Hsiao],
Hang, H.M.[Hsueh-Ming],
End-to-End Learned Image Compression with Augmented Normalizing Flows,
CLIC21(1931-1935)
IEEE DOI
2109
Image coding, Stacking, Low-pass filters, Transforms, Predictive models
BibRef
Suzuki, A.[Akifumi],
Akutsu, H.[Hiroaki],
Naruko, T.[Takahiro],
Tsubota, K.[Koki],
Aizawa, K.[Kiyoharu],
Learned Image Compression with Super-Resolution Residual Modules and
DISTS Optimization,
CLIC21(1906-1910)
IEEE DOI
2109
Image quality, Measurement, Visualization, Image coding,
Superresolution, Bit rate, Decoding
BibRef
Gao, Y.X.[Yi-Xin],
Wu, Y.[Yaojun],
Guo, Z.[Zongyu],
Zhang, Z.Z.[Zhi-Zheng],
Chen, Z.B.[Zhi-Bo],
Perceptual Friendly Variable Rate Image Compression,
CLIC21(1916-1920)
IEEE DOI
2109
Measurement, Training, Visualization, Adaptation models,
Image coding, Rate-distortion
BibRef
Islam, K.[Khawar],
Dang, L.M.[L. Minh],
Lee, S.[Sujin],
Moon, H.[Hyeonjoon],
Image Compression with Recurrent Neural Network and Generalized
Divisive Normalization,
CLIC21(1875-1879)
IEEE DOI
2109
Image coding, Recurrent neural networks, Quantization (signal),
Convolution, Redundancy, Transform coding, Decoding
BibRef
Zhao, J.[Jing],
Li, B.[Bin],
Li, J.H.[Jia-Hao],
Xiong, R.Q.[Rui-Qin],
Lu, Y.[Yan],
A Universal Encoder Rate Distortion Optimization Framework for
Learned Compression,
CLIC21(1880-1884)
IEEE DOI
2109
Image coding, Codecs, Bit rate,
Rate-distortion, Optimization methods
BibRef
Xu, Y.[Yi],
Zhao, M.[Minyi],
Liu, J.[Jing],
Zhang, X.J.[Xin-Jian],
Gao, L.[Longwen],
Zhou, S.[Shuigeng],
Sun, H.Y.[Hu-Yang],
Boosting the Performance of Video Compression Artifact Reduction with
Reference Frame Proposals and Frequency Domain Information,
NTIRE21(213-222)
IEEE DOI
2109
Deep learning, Frequency-domain analysis,
Video compression, Boosting, Spatiotemporal phenomena
BibRef
Peng, W.H.,
Hang, H.M.,
Recent Advances in End-to-End Learned Image and Video Compression,
VCIP20(1-2)
IEEE DOI
2102
Image coding, Transform coding, Standards, Video compression,
Streaming media, MPEG standards, High efficiency video coding
BibRef
Xu, D.,
Lu, G.,
Yang, R.,
Timofte, R.,
Learned image and video compression with deep neural networks,
VCIP20(1-3)
IEEE DOI
2102
Image coding, Data compression, Video compression,
Deep learning, Tutorials
BibRef
Ayzik, S.[Sharon],
Avidan, S.[Shai],
Deep Image Compression Using Decoder Side Information,
ECCV20(XVII:699-714).
Springer DOI
2011
Code, Compression.
WWW Link. Information available only to the decoder. Learn the transformation.
BibRef
Su, R.,
Cheng, Z.,
Sun, H.,
Katto, J.,
Scalable Learned Image Compression With A Recurrent Neural
Networks-Based Hyperprior,
ICIP20(3369-3373)
IEEE DOI
2011
Image coding, Entropy, Quantization (signal), Transform coding,
Entropy coding, Recurrent neural networks, Transforms,
RNN-based hyperprior
BibRef
Guarda, A.F.R.,
Rodrigues, N.M.M.,
Pereira, F.,
Point Cloud Geometry Scalable Coding With a Single End-to-End Deep
Learning Model,
ICIP20(3354-3358)
IEEE DOI
2011
Encoding, Geometry, Decoding,
Transform coding, Standards, Training, Point cloud coding,
quality scalability
BibRef
Singh, S.,
Abu-El-Haija, S.,
Johnston, N.,
Ballé, J.,
Shrivastava, A.,
Toderici, G.,
End-to-End Learning of Compressible Features,
ICIP20(3349-3353)
IEEE DOI
2011
Image coding, Task analysis, Training, Quantization (signal),
Distortion, Entropy, Principal component analysis,
Neural networks
BibRef
Bhagat, S.[Sarthak],
Uppal, S.[Shagun],
Yin, Z.Y.[Zhu-Yun],
Lim, N.L.[Neng-Li],
Disentangling Multiple Features in Video Sequences Using Gaussian
Processes in Variational Autoencoders,
ECCV20(XXIII:102-117).
Springer DOI
2011
Multiple features, static or dynamic, can be disentangled.
BibRef
Lu, G.[Guo],
Cai, C.L.[Chun-Lei],
Zhang, X.Y.[Xiao-Yun],
Chen, L.[Li],
Ouyang, W.L.[Wan-Li],
Xu, D.[Dong],
Gao, Z.Y.[Zhi-Yong],
Content Adaptive and Error Propagation Aware Deep Video Compression,
ECCV20(II:456-472).
Springer DOI
2011
BibRef
Hu, Z.H.[Zhi-Hao],
Chen, Z.H.[Zheng-Hao],
Xu, D.[Dong],
Lu, G.[Guo],
Ouyang, W.L.[Wan-Li],
Gu, S.H.[Shu-Hang],
Improving Deep Video Compression by Resolution-adaptive Flow Coding,
ECCV20(II:193-209).
Springer DOI
2011
BibRef
Sun, W.Y.[Wen-Yu],
Tang, C.[Chen],
Li, W.[Weigui],
Yuan, Z.Q.[Zhu-Qing],
Yang, H.Z.[Hua-Zhong],
Liu, Y.[Yongpan],
High-quality Single-model Deep Video Compression with Frame-Conv3D and
Multi-frame Differential Modulation,
ECCV20(XXX: 239-254).
Springer DOI
2010
BibRef
Xu, J.,
Lytchier, A.,
Cursio, C.,
Kollias, D.,
Besenbruch, C.,
Zafar, A.,
Efficient Context-Aware Lossy Image Compression,
CLIC20(552-554)
IEEE DOI
2008
Context modeling, Image coding, Training, Pipelines,
Decoding, Computational modeling
BibRef
Sun, H.,
Liu, C.,
Katto, J.,
Fan, Y.,
An Image Compression Framework with Learning-based Filter,
CLIC20(602-606)
IEEE DOI
2008
Image color analysis, Principal component analysis, Image coding,
Image reconstruction, Distortion, Correlation, Covariance matrices
BibRef
Tao, H.,
Qian, J.,
Yu, L.,
Wang, H.,
Zhang, W.,
Li, Z.,
Wang, N.,
Zeng, X.,
Post-Processing Network Based on Dense Inception Attention for Video
Compression,
CLIC20(547-551)
IEEE DOI
2008
Encoding, Video compression, Video coding, Image coding,
Feature extraction, Kernel, Standards
BibRef
Feng, R.,
Wu, Y.,
Guo, Z.,
Zhang, Z.,
Chen, Z.,
Learned Video Compression with Feature-level Residuals,
CLIC20(529-532)
IEEE DOI
2008
Image coding, Training, Optical imaging, Adaptive optics,
Motion compensation, Optical distortion, Decoding
BibRef
Akutsu, H.,
Suzuki, A.,
Zhong, Z.,
Aizawa, K.,
Ultra Low Bitrate Learned Image Compression by Selective Detail
Decoding,
CLIC20(524-528)
IEEE DOI
2008
Decoding, Image coding, Entropy, Training, Bit rate,
Random access memory, Indexes
BibRef
He, G.,
Wu, C.,
Li, L.,
Zhou, J.,
Wang, X.,
Zheng, Y.,
Yu, B.,
Xie, W.,
A Video Compression Framework Using an Overfitted Restoration Neural
Network,
CLIC20(593-597)
IEEE DOI
2008
Video compression, Image restoration, Neural networks, Training,
Decoding, Pattern recognition
BibRef
Zou, N.N.[Nan-Nan],
Zhang, H.L.[Hong-Lei],
Cricri, F.[Francesco],
Tavakoli, H.R.[Hamed R.],
Lainema, J.[Jani],
Aksu, E.[Emre],
Hannuksela, M.[Miska],
Rahtu, E.[Esa],
Learned Video Compression with Intra-Guided Enhancement and Implicit
Motion Information,
CLIC21(1870-1874)
IEEE DOI
2109
BibRef
Earlier:
End-to-End Learning for Video Frame Compression with Self-Attention,
CLIC20(580-584)
IEEE DOI
2008
Image coding, Computational modeling, Pipelines, MIMICs, Video compression.
Decoding, Neural networks, Tensile stress, Context modeling,
Image coding, Adaptation models, Probability distribution
BibRef
Lin, C.,
Yao, J.,
Chen, F.,
Wang, L.,
A Spatial RNN Codec for End-to-End Image Compression,
CVPR20(13266-13274)
IEEE DOI
2008
Image coding, Quantization (signal), Standards, Entropy,
Computational modeling, Redundancy, Transforms
BibRef
Chin, T.,
Ding, R.,
Zhang, C.,
Marculescu, D.,
Towards Efficient Model Compression via Learned Global Ranking,
CVPR20(1515-1525)
IEEE DOI
2008
Complexity theory, Art, Drones,
Autonomous robots, Computational modeling
BibRef
Quach, M.,
Valenzise, G.,
Dufaux, F.,
Learning Convolutional Transforms for Lossy Point Cloud Geometry
Compression,
ICIP19(4320-4324)
IEEE DOI
1910
point cloud geometry compression, convolutional neural network,
rate-distortion optimization
BibRef
Mentzer, F.[Fabian],
Agustsson, E.[Eirikur],
Tschannen, M.[Michael],
Timofte, R.[Radu],
Van Gool, L.J.[Luc J.],
Practical Full Resolution Learned Lossless Image Compression,
CVPR19(10621-10630).
IEEE DOI
2002
BibRef
Yang, J.,
Yang, C.,
Ma, Y.,
Liu, S.,
Wang, R.,
Learned Low Bit-rate Image Compression with Adversarial Mechanism,
CLIC20(575-579)
IEEE DOI
2008
Image coding, Image reconstruction, Decoding, Training, Entropy,
Estimation, Distortion
BibRef
Huang, C.C.[Ching-Chun],
Nguyen, T.P.[Thanh-Phat],
Lai, C.T.[Chen-Tung],
Multi-Channel Multi-Loss Deep Learning Based Compression Model of
Color Images,
ICIP19(4524-4528)
IEEE DOI
1910
CNN, Deep image compression, Color shift reduction
BibRef
Lin, J.,
Liu, D.,
Li, H.,
Wu, F.,
M-LVC: Multiple Frames Prediction for Learned Video Compression,
CVPR20(3543-3551)
IEEE DOI
2008
Video compression, Image coding, Motion compensation, Entropy,
Encoding, Motion estimation, Transforms
BibRef
Yang, R.,
Mentzer, F.,
Van Gool, L.J.,
Timofte, R.,
Learning for Video Compression With Hierarchical Quality and
Recurrent Enhancement,
CVPR20(6627-6636)
IEEE DOI
2008
Image coding, Video compression, Decoding, Bidirectional control,
Streaming media, Rate-distortion, Correlation
BibRef
Mentzer, F.,
Van Gool, L.J.,
Tschannen, M.,
Learning Better Lossless Compression Using Lossy Compression,
CVPR20(6637-6646)
IEEE DOI
2008
Image coding, Image reconstruction, Probabilistic logic,
Entropy coding, Bit rate, Decoding, Transform coding
BibRef
Luo, A.[Ao],
Sun, H.M.[He-Ming],
Liu, J.M.[Jin-Ming],
Katto, J.[Jiro],
Memory-Efficient Learned Image Compression with Pruned Hyperprior
Module,
ICIP22(3061-3065)
IEEE DOI
2211
Performance evaluation, Image coding, Costs, Deconvolution,
Image edge detection, Memory management, Rate-distortion,
Model Pruning
BibRef
Cheng, Z.X.[Zheng-Xue],
Sun, H.M.[He-Ming],
Takeuchi, M.[Masaru],
Katto, J.[Jiro],
Learned Image Compression With Discretized Gaussian Mixture
Likelihoods and Attention Modules,
CVPR20(7936-7945)
IEEE DOI
2008
BibRef
And: A1, A2, A4, Only:
Low Bitrate Image Compression with Discretized Gaussian Mixture
Likelihoods,
CLIC20(543-546)
IEEE DOI
2008
Image coding, Entropy, Standards, Visualization,
Training, Redundancy, Transform coding.
Convolution, Training, Decoding,
Pattern recognition, Bit rate
BibRef
Djelouah, A.,
Campos, J.,
Schaub-Meyer, S.,
Schroers, C.[Christopher],
Neural Inter-Frame Compression for Video Coding,
ICCV19(6420-6428)
IEEE DOI
2004
data compression, decoding, image sequences, interpolation,
learning (artificial intelligence), motion compensation, Distortion
BibRef
Lucas, A.,
Lopez-Tapia, S.,
Molina, R.,
Katsaggelos, A.K.,
Efficient Fine-Tuning of Neural Networks for Artifact Removal in Deep
Learning for Inverse Imaging Problems,
ICIP19(3591-3595)
IEEE DOI
1910
Deep Neural Networks, Image and Video Processing, Inversion,
Fine-tuning, Artifacts, Data Consistency
BibRef
Rippel, O.,
Nair, S.,
Lew, C.,
Branson, S.,
Anderson, A.,
Bourdev, L.,
Learned Video Compression,
ICCV19(3453-3462)
IEEE DOI
2004
data compression, image sequences,
learning (artificial intelligence), motion estimation,
Video codecs
BibRef
Cheng, Z.X.[Zheng-Xue],
Sun, H.M.[He-Ming],
Takeuchi, M.[Masaru],
Katto, J.[Jiro],
Learning Image and Video Compression Through Spatial-Temporal Energy
Compaction,
CVPR19(10063-10072).
IEEE DOI
2002
BibRef
Westland, N.,
Dias, A.S.,
Mrak, M.,
Decision Trees for Complexity Reduction in Video Compression,
ICIP19(2666-2670)
IEEE DOI
1910
Video Coding, Complexity Reduction, Machine Learning, Decision Trees
BibRef
Su, H.,
Tsai, C.,
Wang, Y.,
Xu, Y.,
Machine Learning Accelerated Partition Search for Video Encoding,
ICIP19(2661-2665)
IEEE DOI
1910
Video Coding, Machine Learning, Partition Search, Encoding Speedup, VP9
BibRef
Ho, Y.H.[Yung-Han],
Cho, C.Y.[Chuan-Yuan],
Peng, W.H.[Wen-Hsiao],
Deep Reinforcement Learning for Video Prediction,
ICIP19(604-608)
IEEE DOI
1910
Reinforcement learning, deep video prediction
BibRef
Kumar, S.[Saurabh],
Chaudhuri, S.[Subhasis],
Banerjee, B.[Biplab],
Ali, F.[Feroz],
Onboard Hyperspectral Image Compression Using Compressed Sensing and
Deep Learning,
CVUAV18(II:30-42).
Springer DOI
1905
BibRef
Nakanishi, K.M.[Ken M.],
Maeda, S.I.[Shin-Ichi],
Miyato, T.[Takeru],
Okanohara, D.[Daisuke],
Neural Multi-scale Image Compression,
ACCV18(VI:718-732).
Springer DOI
1906
consists of two networks: multi-scale lossy autoencoder
and parallel multi-scale lossless coder.
BibRef
He, X.Y.[Xiang-Yu],
Cheng, J.[Jian],
Learning Compression from Limited Unlabeled Data,
ECCV18(I: 778-795).
Springer DOI
1810
BibRef
Xu, K.[Kai],
Ren, F.[Fengbo],
CSVideoNet: A Real-Time End-to-End Learning Framework for
High-Frame-Rate Video Compressive Sensing,
WACV18(1680-1688)
IEEE DOI
1806
cameras, compressed sensing, data compression, decoding,
image reconstruction, image resolution,
Streaming media
BibRef
Pavez, E.,
Ortega, A.,
Mukherjee, D.,
Learning separable transforms by inverse covariance estimation,
ICIP17(285-289)
IEEE DOI
1803
Covariance matrices, Discrete cosine transforms, Encoding,
Estimation, Image coding, video coding
BibRef
Shen, H.,
Pan, W.D.[W. David],
Predictive lossless compression of regions of interest in
hyperspectral image via Maximum Correntropy Criterion based Least
Mean Square learning,
ICIP16(2182-2186)
IEEE DOI
1610
Data communication
BibRef
Quijas, J.,
Fuentes, O.,
Removing JPEG blocking artifacts using machine learning,
Southwest14(77-80)
IEEE DOI
1406
data compression
BibRef
Zhan, X.[Xin],
Zhang, R.[Rong],
Yin, D.[Dong],
Hu, A.Z.[An-Zhou],
Hu, W.L.[Wen-Long],
Remote sensing image compression based on double-sparsity dictionary
learning and universal trellis coded quantization,
ICIP13(1665-1669)
IEEE DOI
1402
Dictionary learning
BibRef
Sun, X.Y.[Xiao-Yan],
Wu, F.[Feng],
Classified patch learning for spatially scalable video coding,
ICIP09(2301-2304).
IEEE DOI
0911
BibRef
He, X.F.[Xiao-Fei],
Ji, M.[Ming],
Bao, H.J.[Hu-Jun],
A unified active and semi-supervised learning framework for image
compression,
CVPR09(65-72).
IEEE DOI
0906
Learn which pixels predict the color for others.
BibRef
Lampert, C.H.[Christoph H.],
Machine Learning for Video Compression: Macroblock Mode Decision,
ICPR06(I: 936-940).
IEEE DOI
0609
BibRef
Simard, P.Y.,
Burges, C.J.C.,
Steinkraus, D.,
Malvar, H.S.,
Image compression with on-line and off-line learning,
ICIP03(II: 259-262).
IEEE DOI
0312
BibRef
Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Motion Coding, Video Coding, Evaluations, Surveys .