5.4.1.2 Neural Networks, Learning for Image Compression

Chapter Contents (Back)
Image Coding. Image Compression. Neural Nets. Learning.
See also Learning, Neural Nets for Coding, Compression in Video.

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Neural Network Approaches To Image Compression,
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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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Xu, M.[Mai], Li, S.X.[Sheng-Xi], Lu, J.H.[Jian-Hua], Zhu, W.W.[Wen-Wu],
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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Sparse representation of texture patches for low bit-rate image compression,
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Gao, F.Y.[Fang-Yuan], Deng, X.[Xin], Gao, C.[Chao], Xu, M.[Mai],
ULcompress: A Unified low bit-rate image Compression Framework via Invertible Image Representation,
ICIP23(2095-2099)
IEEE DOI 2312
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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

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
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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

Braca, P., Lazzeretti, R., Marano, S., Matta, V.,
Learning With Privacy in Consensus + Obfuscation,
SPLetters(23), No. 9, September 2016, pp. 1174-1178.
IEEE DOI 1609
data privacy BibRef

Alshehri, S.A.,
Neural network technique for image compression,
IET-IP(10), No. 3, 2016, pp. 222-226.
DOI Link 1603
data compression BibRef

Jiang, F., Tao, W., Liu, S., Ren, J., Guo, X., Zhao, D.,
An End-to-End Compression Framework Based on Convolutional Neural Networks,
CirSysVideo(28), No. 10, October 2018, pp. 3007-3018.
IEEE DOI 1811
Image coding, Transform coding, Image reconstruction, Neural networks, Codecs, Convolutional codes, convolutional neural networks (CNNs) BibRef

Ding, J., Wang, I., Chen, H.,
Improved Efficiency on Adaptive Arithmetic Coding for Data Compression Using Range-Adjusting Scheme, Increasingly Adjusting Step, and Mutual-Learning Scheme,
CirSysVideo(28), No. 12, December 2018, pp. 3412-3423.
IEEE DOI 1812
Image coding, Probability distribution, Transform coding, Entropy coding, Adaptation models, Data compression, lossless image compression by edge-directed prediction 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

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

Dumas, T., Roumy, A., Guillemot, C.,
Context-Adaptive Neural Network-Based Prediction for Image Compression,
IP(29), No. 1, 2020, pp. 679-693.
IEEE DOI 1910
convolutional neural nets, data compression, image texture, set theory, video codecs, video coding, image compression, neural networks 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

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

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

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

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

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.Y.[Tian-Yu], 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

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

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

Guo, J.Y.[Jin-Yang], Xu, D.[Dong], Lu, G.[Guo],
CBANet: Toward Complexity and Bitrate Adaptive Deep Image Compression Using a Single Network,
IP(32), 2023, pp. 2049-2062.
IEEE DOI 2304
Image coding, Bit rate, Decoding, Computational complexity, Computational modeling, Adaptive systems, Rate-distortion, dynamic computational complexity BibRef

Fu, H.S.[Hai-Sheng], Liang, F.[Feng], Lin, J.P.[Jian-Ping], Li, B.[Bing], Akbari, M.[Mohammad], Liang, J.[Jie], Zhang, G.[Guohe], Liu, D.[Dong], Tu, C.J.[Cheng-Jie], Han, J.N.[Jing-Ning],
Learned Image Compression With Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules,
IP(32), 2023, pp. 2063-2076.
IEEE DOI 2304
Image coding, Entropy, Entropy coding, Decoding, Correlation, Context modeling, Complexity theory, residual network BibRef

Bao, Y.[Youneng], Meng, F.Y.[Fan-Yang], Li, C.[Chao], Ma, S.W.[Si-Wei], Tian, Y.H.[Yong-Hong], Liang, Y.S.[Yong-Sheng],
Nonlinear Transforms in Learned Image Compression From a Communication Perspective,
CirSysVideo(33), No. 4, April 2023, pp. 1922-1936.
IEEE DOI 2304
Transforms, Modulation, Image coding, Communication systems, Quantization (signal), Mathematical models, Context modeling, nonlinear transform BibRef

Yang, Y.[Yibo], Mandt, S.[Stephan], Theis, L.[Lucas],
An Introduction to Neural Data Compression,
FTCGV(15), No. 2, 2023, pp. 113-200.
DOI Link 2305
Survey, Compression. BibRef

Wang, S.R.[Shu-Run], Wang, Z.[Zhao], Wang, S.Q.[Shi-Qi], Ye, Y.[Yan],
Deep Image Compression Toward Machine Vision: A Unified Optimization Framework,
CirSysVideo(33), No. 6, June 2023, pp. 2979-2989.
IEEE DOI 2306
Visualization, Image coding, Machine vision, Transform coding, Task analysis, Feature extraction, Bit rate, Image compression, machine vision BibRef

Lee, S.[Soonbin], Jeong, J.B.[Jong-Beom], Ryu, E.S.[Eun-Seok],
Entropy-Constrained Implicit Neural Representations for Deep Image Compression,
SPLetters(30), 2023, pp. 663-667.
IEEE DOI 2307
Image coding, Entropy, Computational modeling, Training, Data models, Neural networks, Distortion, Image compression, model compression, implicit neural representation BibRef

Fu, H.S.[Hai-Sheng], Liang, F.[Feng], Liang, J.[Jie], Li, B.L.[Bing-Lin], Zhang, G.[Guohe], Han, J.N.[Jing-Ning],
Asymmetric Learned Image Compression With Multi-Scale Residual Block, Importance Scaling, and Post-Quantization Filtering,
CirSysVideo(33), No. 8, August 2023, pp. 4309-4321.
IEEE DOI 2308
Image coding, Decoding, Complexity theory, Quantization (signal), Entropy coding, Bit rate, Measurement BibRef

Luo, S.[Sihui], Fang, G.F.[Gong-Fan], Song, M.L.[Ming-Li],
Deep semantic image compression via cooperative network pruning,
JVCIR(95), 2023, pp. 103897.
Elsevier DOI 2309
Deep image compression, Network pruning, Semantic perception BibRef

Duan, W.H.[Wen-Hong], Chang, Z.[Zheng], Jia, C.M.[Chuan-Min], Wang, S.S.[Shan-She], Ma, S.W.[Si-Wei], Song, L.[Li], Gao, W.[Wen],
Learned Image Compression Using Cross-Component Attention Mechanism,
IP(32), 2023, pp. 5478-5493.
IEEE DOI 2310
BibRef

Zhang, G.[Gai], Zhang, X.F.[Xin-Feng], Tang, L.[Lv],
Enhanced Quantified Local Implicit Neural Representation for Image Compression,
SPLetters(30), 2023, pp. 1742-1746.
IEEE DOI 2312
BibRef


Park, J.[Jongmin], Lee, J.Y.[Joo-Young], Kim, M.C.[Mun-Churl],
COMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale Spatial Scalability,
ICCV23(12780-12789)
IEEE DOI 2401
BibRef

Tao, L.F.[Lv-Fang], Gao, W.[Wei], Li, G.[Ge], Zhang, C.H.[Chen-Hao],
AdaNIC: Towards Practical Neural Image Compression via Dynamic Transform Routing,
ICCV23(16833-16842)
IEEE DOI 2401
BibRef

Yang, Y.[Yibo], Mandt, S.[Stephan],
Computationally-Efficient Neural Image Compression with Shallow Decoders,
ICCV23(530-540)
IEEE DOI Code:
WWW Link. 2401
BibRef

Presta, A.[Alberto], Fiandrotti, A.[Attilio], Tartaglione, E.[Enzo], Grangetto, M.[Marco],
A Differentiable Entropy Model for Learned Image Compression,
CIAP23(I:328-339).
Springer DOI 2312
BibRef

Yang, Z.[Zheng], Wang, R.G.[Rong-Gang],
Improving Learned Invertible Coding with Invertible Attention and Back-Projection,
ICIP23(3349-3353)
IEEE DOI 2312
BibRef

Haase, P.[Paul], Pfaff, J.[Jonathan], Schwarz, H.[Heiko], Marpe, D.[Detlev], Wiegand, T.[Thomas],
Bitrate-Performance Optimized Model Training for the Neural Network Coding (NNC) Standard,
ICIP23(3245-3249)
IEEE DOI 2312
BibRef

Foroutan, Y.[Yalda], Harell, A.[Alon], de Andrade, A.[Anderson], Bajic, I.V.[Ivan V.],
Base Layer Efficiency in Scalable Human-Machine Coding,
ICIP23(3299-3303)
IEEE DOI 2312
Efficiency of the coding intended for machine, not human, use. BibRef

Minnen, D.[David], Johnston, N.[Nick],
Advancing the Rate-Distortion-Computation Frontier for Neural Image Compression,
ICIP23(2940-2944)
IEEE DOI 2312
BibRef

Ye, Y.[Yun], Pan, Y.J.[Yan-Jie], Jiang, Q.[Qually], Lu, M.[Ming], Fang, X.R.[Xiao-Ran], Xu, B.[Beryl],
Frequency-Aware Re-Parameterization for Over-Fitting Based Image Compression,
ICIP23(2310-2314)
IEEE DOI 2312
BibRef

Hu, Y.T.[Yu-Ting], Tan, W.[Wen], Meng, F.Y.[Fan-Yang], Liang, Y.S.[Yong-Sheng],
A Decoupled Spatial-Channel Inverted Bottleneck For Image Compression,
ICIP23(1740-1744)
IEEE DOI 2312
BibRef

Munna, T.A.[Tahsir Ahmed], Ascenso, J.[Joăo],
Complexity Scalable Learning-Based Image Decoding,
ICIP23(1860-1864)
IEEE DOI 2312
BibRef

Herglotz, C.[Christian], Brand, F.[Fabian], Regensky, A.[Andy], Rievel, F.[Felix], Kaup, A.[André],
Processing Energy Modeling For Neural Network Based Image Compression,
ICIP23(2390-2394)
IEEE DOI 2312
Energy use in NN based compression. BibRef

Brand, F.[Fabian], Kopte, A.[Alexander], Fischer, K.[Kristian], Kaup, A.[André],
Spatially-Adaptive Learning-Based Image Compression with Hierarchical Multi-Scale Latent Spaces,
ICIP23(1660-1664)
IEEE DOI 2312
BibRef

Meng, X.D.[Xian-Dong], Zhu, S.Y.[Shu-Yuan], Ma, S.W.[Si-Wei], Zeng, B.[Bing],
Learned Image Compression with Large Capacity and Low Redundancy of Latent Representation,
ICIP23(1640-1644)
IEEE DOI 2312
BibRef

Hojjat, A.[Ali], Haberer, J.[Janek], Landsiedel, O.[Olaf],
ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Training,
NTIRE23(1130-1139)
IEEE DOI 2309
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.H.[Yi-Hui], Shi, Y.[Yibo], Wang, J.[Jing],
Content-Oriented Learned Image Compression,
ECCV22(XIX:632-647).
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

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, 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, 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

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

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.L.[Dai-Lan], 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

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

Kim, J.H.[Jun-Hyuk], Heo, B.[Byeongho], Lee, J.S.[Jong-Seok],
Joint Global and Local Hierarchical Priors for Learned Image Compression,
CVPR22(5982-5991)
IEEE DOI 2210
Image coding, Computational modeling, Redundancy, Rate-distortion, Transformers, Entropy, Probability distribution, Low-level vision 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.R.[Wen-Rui], Li, C.L.[Cheng-Lin], 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.[Joăo], 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

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.J.[Yao-Jun], Guo, Z.Y.[Zong-Yu], 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

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

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

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

Agustsson, E.[Eirikur], Minnen, D.[David], Toderici, G.[George], Mentzer, F.[Fabian],
Multi-Realism Image Compression with a Conditional Generator,
CVPR23(22324-22333)
IEEE DOI 2309
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

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

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

Li, C.X.[Chong-Xin], Luo, J.X.[Ji-Xiang], Dai, W.R.[Wen-Rui], Li, C.L.[Cheng-Lin], Zou, J.N.[Jun-Ni], Xiong, H.K.[Hong-Kai],
Spatial-Channel Context-Based Entropy Modeling for End-to-end Optimized Image Compression,
VCIP20(222-225)
IEEE DOI 2102
Reduce spatial redundancy, improve reconstruction. Entropy, Image coding, Context modeling, Transforms, Entropy coding, Decoding, Solid modeling, End-to-end optimized image compression, artificial neural networks 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

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

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

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

Parodi, G., Passaggio, F.,
Size-adaptive neural network for image compression,
ICIP94(III: 945-947).
IEEE DOI 9411
BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Block Coding, General Techniques and Issues .


Last update:Jan 30, 2024 at 20:33:16