5.5.1 Learning, Neural Nets for Coding, Compression

Chapter Contents (Back)
Compression. Neural Nets. Learning.

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
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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
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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
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Earlier: A1, A3, A4, Only:
Sparse representation of texture patches for low bit-rate image compression,
VCIP12(1-6).
IEEE DOI 1302
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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
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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.[Anhong], 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.[Dandan], 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.[Zixi], 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


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], 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.[Huyang],
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, Computer architecture, 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, Computer architecture, 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

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.[Heming], 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, Evaluations, Surveys .


Last update:Nov 30, 2021 at 22:19:38