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Image coding, Probability distribution, Transform coding,
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Compressed image restoration, sparse modeling,
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Compression artifacts removal, benchmark, side information,
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Image compression, convolutional autoencoder,
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Convolutional neural network, deep learning, image compression
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Lossy image compression, convolutional neural networks, tucker decomposition
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2007
Image coding, Cameras, Context modeling, Tools, Codecs,
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Image coding, Optimization, Image quality, Training, Task analysis,
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1910
Image coding, Laplace equations, Semantics,
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image compression, deep learning, Laplacian pyramid,
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2106
Autoencoder, Lossy compression, Convolution Neural Network,
Hyperspectral images, Learning based compression
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2106
Generative adversarial network,
Remote sensing image compression, Symmetrical lattice, Cooperative learning
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2108
Image coding, Image reconstruction, Inverse problems,
Generative adversarial networks, Generators, Deep learning, inverse problem
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2109
Deep image compression, Multi-stage representation,
Data-dependent probability model, Convolutional neural network
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2109
Image coding, Decoding, Task analysis, Convolutional codes,
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range-dependent normalization
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Chen, Z.B.[Zhi-Bo],
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2109
Image coding, Task analysis, Bit rate, Image reconstruction,
Amplitude modulation, Semantics, Computational modeling,
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Guo, Z.Y.[Zong-Yu],
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CirSysVideo(32), No. 4, April 2022, pp. 2329-2341.
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2204
Context modeling, Predictive models, Image coding, Entropy,
Correlation, Entropy coding, Transforms, Learned image compression,
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Mei, Y.X.[Yi-Xin],
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IEEE DOI
2208
Image coding, Standards, Redundancy, Streaming media, Scalability,
Image reconstruction, Spatial resolution,
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Li, D.W.[Dao-Wen],
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Elsevier DOI
2208
Deep image compression, Multi-scale deformable convolution, Spatial attention
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Adaptive Context Modeling for Arithmetic Coding Using Perceptrons,
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IEEE DOI
2212
Context modeling, Training, Adaptation models, Table lookup, Symbols,
Encoding, Codes, Adaptive arithmetic coding, neural context modeling
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Tang, Z.[Zhisen],
Wang, H.[Hanli],
Yi, X.K.[Xiao-Kai],
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Kwong, S.[Sam],
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Joint Graph Attention and Asymmetric Convolutional Neural Network for
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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
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Guo, J.Y.[Jin-Yang],
Xu, D.[Dong],
Lu, G.[Guo],
CBANet: Toward Complexity and Bitrate Adaptive Deep Image Compression
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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
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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.
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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
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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
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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
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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
Tsubota, K.[Koki],
Aizawa, K.[Kiyoharu],
Content-Adaptive Optimization Framework for Universal Deep Image
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IEICE(E108-D), No. 2, February 2024, pp. 201-211.
WWW Link.
2402
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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
Liang, Y.X.[Yan-Xia],
Zhao, M.[Meng],
Liu, X.[Xin],
Jiang, J.[Jing],
Lu, G.Y.[Guang-Yue],
Jia, T.[Tong],
An adaptive image compression algorithm based on joint clustering
algorithm and deep learning,
IET-IPR(18), No. 3, 2024, pp. 829-837.
DOI Link
2402
image processing, neural networks, pixel clustering
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Sui, Y.[Yang],
Ding, D.[Ding],
Pan, X.[Xiang],
Xu, X.Z.[Xiao-Zhong],
Liu, S.[Shan],
Yuan, B.[Bo],
Chen, Z.Z.[Zhen-Zhong],
Corner-to-Center long-range context model for efficient learned image
compression,
JVCIR(98), 2024, pp. 103990.
Elsevier DOI
2402
Learned image compression, Context model, Transformer
BibRef
Li, S.H.[Shao-Hui],
Dai, W.R.[Wen-Rui],
Fang, Y.[Yimian],
Zheng, Z.Y.[Zi-Yang],
Fei, W.[Wen],
Xiong, H.K.[Hong-Kai],
Zhang, W.[Wei],
Revisiting Learned Image Compression With Statistical Measurement of
Latent Representations,
CirSysVideo(34), No. 4, April 2024, pp. 2891-2907.
IEEE DOI
2404
Image coding, Transforms, Robustness, Quantization (signal),
Transform coding, Entropy coding, Visualization, post-training pruning
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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
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ICCV23(530-540)
IEEE DOI Code:
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2401
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Presta, A.[Alberto],
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Tartaglione, E.[Enzo],
Grangetto, M.[Marco],
A Differentiable Entropy Model for Learned Image Compression,
CIAP23(I:328-339).
Springer DOI
2312
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Yang, Z.[Zheng],
Wang, R.G.[Rong-Gang],
Improving Learned Invertible Coding with Invertible Attention and
Back-Projection,
ICIP23(3349-3353)
IEEE DOI
2312
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Haase, P.[Paul],
Pfaff, J.[Jonathan],
Schwarz, H.[Heiko],
Marpe, D.[Detlev],
Wiegand, T.[Thomas],
Bitrate-Performance Optimized Model Training for the Neural Network
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ICIP23(3245-3249)
IEEE DOI
2312
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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.
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Minnen, D.[David],
Johnston, N.[Nick],
Advancing the Rate-Distortion-Computation Frontier for Neural Image
Compression,
ICIP23(2940-2944)
IEEE DOI
2312
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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
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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
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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
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
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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],
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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He, X.Y.[Xiang-Yu],
Cheng, J.[Jian],
Learning Compression from Limited Unlabeled Data,
ECCV18(I: 778-795).
Springer DOI
1810
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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
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Quijas, J.,
Fuentes, O.,
Removing JPEG blocking artifacts using machine learning,
Southwest14(77-80)
IEEE DOI
1406
data compression
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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
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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.
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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
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Parodi, G.,
Passaggio, F.,
Size-adaptive neural network for image compression,
ICIP94(III: 945-947).
IEEE DOI
9411
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Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Block Coding, General Techniques and Issues .