5.4.3.6 Image Quantization, Quantization of Images

Max, J.,
Quantizing for Minimum Distortion,
IT(6), No. 1, March 1960, pp. 1-6. ?? BibRef 6003

Lippel, B., Kurland, M., Marsh, A.H.,
Ordered Dither Patterns for Coarse Quantization of Pictures,
PIEEE(59), No. 3, March 1971, pp. 429-431. BibRef 7103

Carter, W.H., Brown, V.D.,
A Signal-Dependent Error Arising in Digitally Processed Images Due to Quantization,
TC(21), No. 12, December 1972, pp. 1380-1384. BibRef 7212

Mori, S.[Sumio],
Shaded picture signal processing system and method,
US_Patent4,366,507, Dec 28, 1982
WWW Link. BibRef 8212

Schafer, R.,
Design of Adaptive and Nonadaptive Quantizers Using Subjective Criteria,
SP(5), 1983, pp. 333-345. BibRef 8300

Kazakos, D.,
New Results on Robust Quantization,
Commun(31), 1983, pp. 965-974. BibRef 8300

Kieffer, J.C.,
Uniqueness of Locally Optimal Quantizer for Log-Concave Density and Convex Error Weighting Functions,
IT(29), 1983, pp. 42-47. BibRef 8300

Cambanis, S., Gerr, N.L.,
A Simple Class of Asymptotically Optimal Quantizers,
IT(29), 1983, pp. 664-676. BibRef 8300

Judell, N., Scharf, L.,
A Simple Derivation of Lloyd's Classical Result for the Optimum Scalar Quantizer,
IT(32), 1986, pp. 326-328.
See also Least Squares Quantization in PCM. BibRef 8600

Swaszek, P.F.,
Asymptotic Performance of Unrestricted Polar Quantizers,
IT(32), 1986, pp. 330-333. BibRef 8600

Li, Q.[Qi], Swaszek, P.F.,
One-pass vector quantizer design by sequential pruning of the training data,
ICIP95(III: 105-108).
IEEE DOI 9510
BibRef

Werman, M., Peleg, S.,
Gray Level Requantization,
CVGIP(43), No. 1, July 1988, pp. 81-87.
Elsevier DOI BibRef 8807

Peleg, S., Werman, M., and Rom, H.,
A Unified Approach to the Change of Resolution: Space and Gray Level,
PAMI(11), No. 7, July 1989, pp. 739-742.
IEEE DOI BibRef 8907

Ishida, S.[Shinichi], Sakamoto, M.[Masahiro], Shinada, Y.[Yasuyuki], Ono, T.[Takeshi],
Image processing method and apparatus,
US_Patent4,969,052, Nov 6, 1990
WWW Link. BibRef 9011
And:
Image processing method and apparatus with reduction of granular noise, etc.,
US_Patent5,159,470, Oct 27, 1992
WWW Link. BibRef

Tanioka, H.[Hiroshi], Yamada, Y.[Yasuhiro],
Apparatus for performing gradation processing on image data,
US_Patent5,121,447, Jun 9, 1992
WWW Link. BibRef 9206

Katayama, A.[Akihiro], Ohsawa, H.[Hidefumi], Fukuhara, A.[Akiko],
Image treatment method and apparatus with error dispersion and controllable quantization,
US_Patent5,325,448, Jun 28, 1994
WWW Link. BibRef 9406

Nguyen, T.B., Oommen, B.J.,
Moment-Preserving Piecewise-Linear Approximations of Signals and Images,
PAMI(19), No. 1, January 1997, pp. 84-91.
IEEE DOI 9702
BibRef

Ortega, A., Vetterli, M.,
Adaptive Scalar Quantization without Side Information,
IP(6), No. 5, May 1997, pp. 665-676.
IEEE DOI
HTML Version. 9705
BibRef
Earlier:
Adaptive quantization without side information,
ICIP94(III: 856-860).
IEEE DOI 9411
BibRef

Ortega, A.,
Optimization Techniques for Adaptive Quantization of Image and Video under Delay Constraints,
Ph.D.Thesis, Dept. of Electrical Engineering, Columbia University, June 1994.
HTML Version. BibRef 9406

Sakazawa, S.[Shigeyuki], Hamada, T.[Takahiro], Matsumoto, S.[Shuichi],
Quantizer designed by using human visual sensitivity,
US_Patent5,557,276, Sep 17, 1996
WWW Link. BibRef 9609

Chung, K.L., Hong, K.B.,
Level Compression-Based Image Representation and Its Applications,
PR(31), No. 3, March 1998, pp. 327-332.
Elsevier DOI 9802
BibRef

Hung, A.C., Meng, T.H.,
Multidimensional Rotations for Robust Quantization of Image Data,
IP(7), No. 1, January 1998, pp. 1-12.
IEEE DOI 9801
BibRef

Blasiak, D., Shen, J., Chan, W.Y.,
Generalized Scalar Quantizer Design Using Dynamic Programming,
SPLetters(6), No. 5, May 1999, pp. 103.
IEEE Top Reference. BibRef 9905

Kämpke, T.[Thomas], Kober, R.[Rudolf],
Discrete signal quantization,
PR(32), No. 4, April 1999, pp. 619-634.
Elsevier DOI BibRef 9904

Wang, G., Li, Y.,
Axiomatic Approach for Quantification of Image Resolution,
SPLetters(6), No. 10, October 1999, pp. 257.
IEEE Top Reference. BibRef 9910

Malo, J., Ferri, F.J., Albert, J.V., Soret, J., Artigas, J.M.,
The role of perceptual contrast non-linearities in image transform quantization,
IVC(18), No. 3, February 2000, pp. 233-246.
Elsevier DOI 0001
BibRef

Malo, J., Ferri, F.J., Albert, J.V., Artigas, J.M.,
Adaptive motion estimation and video vector quantization based on spatiotemporal non-linearities of human perception,
CIAP97(I: 454-461).
Springer DOI 9709
BibRef

Papamarkos, N.[Nikos], Atsalakis, A.[Antonios],
Gray-Level Reduction Using Local Spatial Features,
CVIU(78), No. 3, June 2000, pp. 336-350.
DOI Link 0006
BibRef

Tsakalides, P., Reveliotis, P., Nikias, C.L.,
Scalar quantisation of heavy-tailed signals,
VISP(147), No. 5, October 2000, pp. 475-484. 0101
BibRef

Cihlar, J., Okouneva, G., Beaubien, J., Latifovic, R.,
A new histogram quantization algorithm for land cover mapping,
JRS(22), No. 11, July 2001, pp. 2151-2169. 0201
BibRef

Srivastava, A.[Anuj], Liu, X.W.[Xiu-Wen], Grenander, U.[Ulf],
Universal Analytical Forms for Modeling Image Probabilities,
PAMI(24), No. 9, September 2002, pp. 1200-1214.
IEEE Abstract. 0209

See also Probability Models for Clutter in Natural Images. BibRef

Srivastava, A., Liu, X., Grenander, U.,
Analytical Image Models and Their Applications,
ECCV02(I: 37 ff.).
Springer DOI 0205

See also Spectral Probability Models for IR Images With Applications to IR Face Recognition. BibRef

Srivastava, A., Liu, X., Grenander, U.,
Analytical Models for Reduced Spectral Representations of Images,
ICIP01(I: 153-156).
IEEE DOI 0108
BibRef

Shelley, P.[Paul], Li, X.B.[Xiao-Bo], Han, B.[Bin],
A hybrid quantization scheme for image compression,
IVC(22), No. 3, 1 March 2004, pp. 203-213.
Elsevier DOI 0402
BibRef

Ohshima, S.[Seiji],
Image processing apparatus and method to reduce gray levels of image,
US_Patent6,956,673, Oct 18, 2005
WWW Link. BibRef 0510

Borodkin, S.M., Borodkin, A.M., Muchnik, I.B.,
Optimal Requantization of Deep Grayscale Images and Lloyd-Max Quantization,
IP(15), No. 2, February 2006, pp. 445-448.
IEEE DOI 0602

See also Least Squares Quantization in PCM. BibRef

Wong, A.[Alexander],
PECSI: A Practical Perceptually-enhanced Compression Framework for Still Images,
IJIG(9), No. 4, October 2009, pp. 511-529.
DOI Link 0911
Downsampling, quantization. Improve perceptual quality of coding. BibRef

Mai, Z., Mansour, H., Mantiuk, R., Nasiopoulos, P., Ward, R.K., Heidrich, W.,
Optimizing a Tone Curve for Backward-Compatible High Dynamic Range Image and Video Compression,
IP(20), No. 6, June 2011, pp. 1558-1571.
IEEE DOI 1106
BibRef

Goyal, V.K.,
Scalar Quantization With Random Thresholds,
SPLetters(18), No. 9, September 2011, pp. 525-528.
IEEE DOI 1108
BibRef

Stern, A.[Adrian], Zeltzer, Y.[Yigal], Rivenson, Y.[Yair],
Quantization error and dynamic range considerations for compressive imaging systems design,
JOSA-A(30), No. 6, June 2013, pp. 1069-1077.
DOI Link 1307
BibRef

Schwarz, S., Rupp, M.,
Predictive Quantization on the Stiefel Manifold,
SPLetters(22), No. 2, February 2015, pp. 234-238.
IEEE DOI 1410
MIMO BibRef

Oh, B.T.[Byung Tae],
An adaptive quantization algorithm without side information for depth map coding,
SP:IC(29), No. 9, 2014, pp. 962-970.
Elsevier DOI 1410
Depth map coding BibRef

Oh, B.T.[Byung Tae],
Enhanced zonal search algorithm for motion estimation in depth-map coding,
SIViP(12), No. 3, March 2018, pp. 523-530.
WWW Link. 1804
BibRef

Le Pendu, M.[Mikael], Guillemot, C.[Christine], Thoreau, D.[Dominique],
Local inverse tone curve learning for high dynamic range image scalable compression,
IP(24), No. 12, December 2015, pp. 5753-5763.
IEEE DOI 1512
HEVC. data compression. BibRef

Le Pendu, M.[Mikael], Guillemot, C.[Christine], Thoreau, D.[Dominique],
Inter-Layer Prediction of Color in High Dynamic Range Image Scalable Compression,
IP(25), No. 8, August 2016, pp. 3585-3596.
IEEE DOI 1608
BibRef
And:
Template based inter-layer prediction for high dynamic range scalable compression,
ICIP15(2974-2978)
IEEE DOI 1512
data compression BibRef

Yu, L.T.[Li-Tao], Huang, Z.[Zi], Shen, F.M.[Fu-Min], Song, J.K.[Jing-Kuan], Shen, H.T.[Heng Tao], Zhou, X.F.[Xiao-Fang],
Bilinear Optimized Product Quantization for Scalable Visual Content Analysis,
IP(26), No. 10, October 2017, pp. 5057-5069.
IEEE DOI 1708
Covariance matrices, Distortion, Encoding, Optimization, Vector quantization, Visualization, Product quantization, bilinear projection, visual content analysis BibRef

Wu, H.H.[Hui-Hui], Dumitrescu, S.[Sorina],
Design of Optimal Fixed-Rate Unrestricted Polar Quantizer for Bivariate Circularly Symmetric Sources,
SPLetters(25), No. 5, May 2018, pp. 715-719.
IEEE DOI 1805
Distortion, Dynamic programming, Heuristic algorithms, Linear programming, Minimization, Signal processing algorithms, unrestricted polar quantization BibRef

Song, X.Y.[Xiao-Ying], Liu, B.[Bing], Huang, Q.J.[Qi-Jun], Hu, R.H.[Rui-Han],
Design of high-resolution quantization scheme with exp-Golomb code applied to compression of special images,
JVCIR(65), 2019, pp. 102684.
Elsevier DOI 1912
High-resolution quantization scheme, Adaptive quantizer, Exp-Golomb code, Medical image, Aerial image, Near-lossless compression BibRef

Pérez-Delgado, M.L.[María-Luisa], Gallego, J.Á.R.[Jesús-Ángel Román],
A two-stage method to improve the quality of quantized images,
RealTimeIP(17), No. 3, June 2020, pp. 581-605.
Springer DOI 2006
BibRef

Li, Z.Y.[Zhi-Yang], Qu, W.Y.[Wen-Yu], Cao, Y.[Yuan], Qi, H.[Heng], Stojmenovic, M.[Milos], Hu, J.[Jia],
Scale balance for prototype-based binary quantization,
PR(106), 2020, pp. 107409.
Elsevier DOI 2006
Approximate nearest neighbor search, High-dimensional vectors, Prototype-based binary quantization BibRef

Yan, X., Fan, Y., Chen, K., Yu, X., Zeng, X.,
QNet: An Adaptive Quantization Table Generator Based on Convolutional Neural Network,
IP(29), 2020, pp. 9654-9664.
IEEE DOI 2011
Quantization (signal), Image coding, Optimization, Transform coding, Rate-distortion, Discrete cosine transforms, structural similarity index measurement (SSIM) BibRef

Wang, C.[Chen], Yang, X.M.[Xiao-Mei], Fei, S.M.[Shao-Min], Zhou, K.[Kai], Gong, X.F.[Xiao-Feng], Du, M.[Miao], Luo, R.S.[Rui-Sen],
Scalar Quantization as Sparse Least Square Optimization,
PAMI(43), No. 5, May 2021, pp. 1678-1690.
IEEE DOI 2104
Quantization (signal), Neural networks, Optimization, Signal processing algorithms, Clustering algorithms, approximation BibRef

Doutsi, E., Fillatre, L., Antonini, M., Tsakalides, P.,
Dynamic Image Quantization Using Leaky Integrate-and-Fire Neurons,
IP(30), 2021, pp. 4305-4315.
IEEE DOI 2104
Quantization (signal), Image reconstruction, Neurons, Visualization, Image coding, Transforms, Mathematical model, time coding BibRef

Doutsi, E., Fillatre, L., Antonini, M., Gaulmin, J.,
Neuro-Inspired Quantization,
ICIP18(689-693)
IEEE DOI 1809
Neurons, Delays, Quantization (signal), Image coding, Decoding, Nickel, Image reconstruction, Visual system, Neuron, Uniform Deadzone Quantization (UDQ) BibRef

Paul, S.[Somdyuti], Norkin, A.[Andrey], Bovik, A.C.[Alan C.],
On visual masking estimation for adaptive quantization using steerable filters,
SP:IC(96), 2021, pp. 116290.
Elsevier DOI 2106
Adaptive quantization, Steerable filtering, Visual masking, AV1 BibRef

Talebi, H.[Hossein], Kelly, D.[Damien], Luo, X.[Xiyang], Dorado, I.G.[Ignacio Garcia], Yang, F.[Feng], Milanfar, P.[Peyman], Elad, M.[Michael],
Better Compression With Deep Pre-Editing,
IP(30), 2021, pp. 6673-6685.
IEEE DOI 2108
Image coding, Transform coding, Training, Entropy, Quantization (signal), Loss measurement, Image quality, pre-filtering BibRef

Swamy, A.S.A.[A. S. Anand], Shylashree, N.,
HDR Image Compression by Multi-Scale down Sampling of Intensity Levels,
IJIG(21), No. 4, October 2021 2021, pp. 2150048.
DOI Link 2110
BibRef

Liu, Y.[Yi], Sidaty, N.[Naty], Hamidouche, W.[Wassim], Déforges, O.[Olivier], Jung, C.[Cheolkon],
Visual Attention-Aware High Dynamic Range Quantization for HEVC Video Coding,
CirSysVideo(32), No. 7, July 2022, pp. 4296-4311.
IEEE DOI 2207
Quantization (signal), Visualization, Encoding, TV, Sensitivity, Production, Mathematical models, High dynamic range (HDR), visual quality BibRef

Dulek, B.[Berkan],
On the Optimality of Sufficient Statistics-Based Quantizers,
PAMI(45), No. 3, March 2023, pp. 3567-3573.
IEEE DOI 2302
Quantization (signal), Estimation, Random variables, Parameter estimation, Distortion, Testing, hypothesis testing BibRef

Xu, W.X.[Wei-Xiang], Li, F.R.[Fan-Rong], Jiang, Y.Y.[Ying-Ying], Yong, A., He, X.Y.[Xiang-Yu], Wang, P.S.[Pei-Song], Cheng, J.[Jian],
Improving Extreme Low-Bit Quantization With Soft Threshold,
CirSysVideo(33), No. 4, April 2023, pp. 1549-1563.
IEEE DOI 2304
Quantization (signal), Training, Neural networks, Convolution, Optimization, Computational modeling, Pipelines, ternary quantization BibRef

Ma, L.[Li], Peng, P.X.[Pei-Xi], Chen, G.Y.[Guang-Yao], Zhao, Y.[Yifan], Dong, S.W.[Si-Wei], Tian, Y.H.[Yong-Hong],
Picking Up Quantization Steps for Compressed Image Classification,
CirSysVideo(33), No. 4, April 2023, pp. 1884-1898.
IEEE DOI 2304
Quantization (signal), Image coding, Transform coding, Training, Neural networks, Sensitivity, Deep learning, Compressed images, image classification BibRef

Zhu, X.S.[Xiao-Su], Song, J.K.[Jing-Kuan], Gao, L.L.[Lian-Li], Gu, X.Y.[Xiao-Yan], Shen, H.T.[Heng Tao],
Revisiting Multi-Codebook Quantization,
IP(32), 2023, pp. 2399-2412.
IEEE DOI 2305
Quantization (signal), Encoding, Training, Image coding, Neural networks, Clustering algorithms, Approximation algorithms, retrieval BibRef

Li, S.H.[Shao-Hui], Li, H.[Han], Dai, W.R.[Wen-Rui], Li, C.L.[Cheng-Lin], Zou, J.[Junni], Xiong, H.K.[Hong-Kai],
Learned Progressive Image Compression With Dead-Zone Quantizers,
CirSysVideo(33), No. 6, June 2023, pp. 2962-2978.
IEEE DOI 2306
Image coding, Quantization (signal), Transforms, Transform coding, Neural networks, Rate-distortion, Optimization, embedded quantization BibRef

Qin, H.T.[Hao-Tong], Ding, Y.[Yifu], Zhang, X.G.[Xiang-Guo], Wang, J.[Jiakai], Liu, X.L.[Xiang-Long], Lu, J.W.[Ji-Wen],
Diverse Sample Generation: Pushing the Limit of Generative Data-Free Quantization,
PAMI(45), No. 10, October 2023, pp. 11689-11706.
IEEE DOI 2310
BibRef

Zhang, X.G.[Xiang-Guo], Qin, H.T.[Hao-Tong], Ding, Y.F.[Yi-Fu], Gong, R.[Ruihao], Yan, Q.H.[Qing-Hua], Tao, R.S.[Ren-Shuai], Li, Y.H.[Yu-Hang], Yu, F.W.[Feng-Wei], Liu, X.L.[Xiang-Long],
Diversifying Sample Generation for Accurate Data-Free Quantization,
CVPR21(15653-15662)
IEEE DOI 2111
Quantization (signal), Neural networks, Training data, Network architecture, Data models, Pattern recognition BibRef

Wang, M.[Min], Zhou, W.G.[Wen-Gang], Yao, X.[Xin], Tian, Q.[Qi], Li, H.Q.[Hou-Qiang],
Towards Codebook-Free Deep Probabilistic Quantization for Image Retrieval,
PAMI(46), No. 1, January 2024, pp. 626-640.
IEEE DOI 2312
BibRef

Cheng, X.[Xin], Wang, J.[Jinwei], Wang, H.[Hao], Luo, X.Y.[Xiang-Yang], Ma, B.[Bin],
Quantization Step Estimation of Color Images Based on Res2Net-C With Frequency Clustering Prior Knowledge,
CirSysVideo(34), No. 1, January 2024, pp. 632-646.
IEEE DOI 2401
BibRef

Du, Y.C.[Yong-Chao], Wang, M.[Min], Zhou, W.G.[Wen-Gang], Li, H.Q.[Hou-Qiang],
Progressive Similarity Preservation Learning for Deep Scalable Product Quantization,
MultMed(26), 2024, pp. 3034-3045.
IEEE DOI 2402
Codes, Quantization (signal), Feature extraction, Training, Semantics, Visualization, Costs, Image retrieval, scalable code length BibRef

Senozan, H.[Hatice], Soylu, B.[Banu],
A flexible non-monotonic discretization method for pre-processing in supervised learning,
PRL(181), 2024, pp. 77-85.
Elsevier DOI 2405
Machine learning, Pre-processing, Binarization, Monotonicity, Classification BibRef

Colbert, I.[Ian], Pappalardo, A.[Alessandro], Petri-Koenig, J.[Jakoba],
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance,
ICCV23(16943-16952)
IEEE DOI 2401
BibRef

Dong, P.[Peijie], Li, L.[Lujun], Wei, Z.[Zimian], Niu, X.[Xin], Tian, Z.L.[Zhi-Liang], Pan, H.[Hengyue],
EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization,
ICCV23(17030-17040)
IEEE DOI 2401
BibRef

Jiang, S.Q.[Shi-Qi], Yuan, H.[Hui], Li, S.[Shuai], Mao, X.L.[Xiao-Long],
Fourier Series and Laplacian Noise-Based Quantization Error Compensation for End-to-End Learning-Based Image Compression,
ICIP23(2975-2979)
IEEE DOI 2312
BibRef

Ge, Z.Q.[Zi-Qing], Jia, C.M.[Chuan-Min], Ma, S.W.[Si-Wei], Gao, W.[Wen],
NUCQ: Non-Uniform Conditional Quantization for Learned Image Compression,
ICIP23(840-844)
IEEE DOI 2312
BibRef

Jeon, G.W.[Geun-Woo], Yu, S.[SeungEun], Lee, J.S.[Jong-Seok],
Integer Quantized Learned Image Compression,
ICIP23(2755-2759)
IEEE DOI 2312
BibRef

Qian, B.[Biao], Wang, Y.[Yang], Hong, R.C.[Ri-Chang], Wang, M.[Meng],
Adaptive Data-Free Quantization,
CVPR23(7960-7968)
IEEE DOI 2309
BibRef

Yvinec, E.[Edouard], Dapogny, A.[Arnaud], Cord, M.[Matthieu], Bailly, K.[Kevin],
SPIQ: Data-Free Per-Channel Static Input Quantization,
WACV23(3858-3867)
IEEE DOI 2302
Data privacy, Quantization (signal), Semantic segmentation, Neural networks, Estimation, Object detection, and un-supervised learning BibRef

Goutham, R.[Rangu], Afrabandpey, H.[Homayun], Cricri, F.[Francesco], Zhang, H.L.[Hong-Lei], Aksu, E.[Emre], Hannuksela, M.[Miska], Tavakoli, H.R.[Hamed R.],
Stochastic Binary-Ternary Quantization for Communication Efficient Federated Computation,
ICIP22(2097-2101)
IEEE DOI 2211
Quantization (signal), Image coding, Computational modeling, Stochastic processes, Transform coding, Switches, quantization BibRef

Sühring, K.[Karsten], Schäfer, M.[Michael], Pfaff, J.[Jonathan], Schwarz, H.[Heiko], Marpe, D.[Detlev], Wiegand, T.[Thomas],
Trellis-Coded Quantization for End-to-End Learned Image Compression,
ICIP22(3306-3310)
IEEE DOI 2211
Training, Video coding, Visualization, Quantization (signal), Image coding, Bit rate, Transform coding, Deep Learning, Trellis-Coded Quantization BibRef

Singh, P.[Praneet], Delp, E.J.[Edward J.], Reibman, A.R.[Amy R.],
Video-Analytics Task-Aware Quad-Tree Partitioning and Quantization for HEVC,
ICIP22(2936-2940)
IEEE DOI 2211
Analytical models, Image coding, Quantization (signal), Visual analytics, Computational modeling, Video compression, task-aware BibRef

Wang, Z.[Zhe], Lin, J.[Jie], Geng, X.[Xue], Aly, M.M.S.[Mohamed M. Sabry], Chandrasekhar, V.[Vijay],
RDO-Q: Extremely Fine-Grained Channel-Wise Quantization via Rate-Distortion Optimization,
ECCV22(XII:157-172).
Springer DOI 2211
BibRef

Park, S.[Sein], Jang, Y.[Yeongsang], Park, E.[Eunhyeok],
Symmetry Regularization and Saturating Nonlinearity for Robust Quantization,
ECCV22(XI:206-222).
Springer DOI 2211
BibRef

van Baalen, M.[Mart], Kahne, B.[Brian], Mahurin, E.[Eric], Kuzmin, A.[Andrey], Skliar, A.[Andrii], Nagel, M.[Markus], Blankevoort, T.[Tijmen],
Simulated Quantization, Real Power Savings,
ECV22(2756-2760)
IEEE DOI 2210
Power demand, Quantization (signal), Neural networks, Hardware, Pattern recognition BibRef

Allenet, T.[Thibault], Briand, D.[David], Bichler, O.[Olivier], Sentieys, O.[Olivier],
Disentangled Loss for Low-Bit Quantization-Aware Training,
ECV22(2787-2791)
IEEE DOI 2210
Training, Visualization, Quantization (signal), Entropy, Pattern recognition BibRef

Liu, Z.C.[Ze-Chun], Cheng, K.T.[Kwang-Ting], Huang, D.[Dong], Xing, E.[Eric], Shen, Z.Q.[Zhi-Qiang],
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation,
CVPR22(4932-4942)
IEEE DOI 2210
Quantization (signal), Codes, Neural networks, Stochastic processes, Estimation, Entropy, Recognition: detection, Representation learning BibRef

Yang, L.[Luwei], Shrestha, R.[Rakesh], Li, W.B.[Wen-Bo], Liu, S.C.[Shuai-Cheng], Zhang, G.F.[Guo-Feng], Cui, Z.P.[Zhao-Peng], Tan, P.[Ping],
SceneSqueezer: Learning to Compress Scene for Camera Relocalization,
CVPR22(8249-8258)
IEEE DOI 2210
Location awareness, Visualization, Solid modeling, Image coding, Quantization (signal), Pose estimation, Robot vision BibRef

Choi, K.[Kanghyun], Lee, H.Y.[Hye Yoon], Hong, D.[Deokki], Yu, J.[Joonsang], Park, N.[Noseong], Kim, Y.[Youngsok], Lee, J.H.[Jin-Ho],
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher,
CVPR22(8301-8311)
IEEE DOI 2210
Training, Deep learning, Data privacy, Quantization (signal), Machine vision, Neural networks, Training data, Vision applications and systems BibRef

Doan, K.D.[Khoa D.], Yang, P.[Peng], Li, P.[Ping],
One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching,
CVPR22(9437-9447)
IEEE DOI 2210
Hash functions, Quantization (signal), Codes, Computational modeling, Estimation, Encoding, retrieval BibRef

Jeon, Y.[Yongkweon], Lee, C.[Chungman], Cho, E.[Eulrang], Ro, Y.[Yeonju],
Mr.BiQ: Post-Training Non-Uniform Quantization based on Minimizing the Reconstruction Error,
CVPR22(12319-12328)
IEEE DOI 2210
Quantization (signal), Computational modeling, Neural networks, Transformers, Pattern recognition, Calibration BibRef

Lee, J.H.[Jae-Han], Jeon, S.[Seungmin], Choi, K.P.[Kwang Pyo], Park, Y.[Youngo], Kim, C.S.[Chang-Su],
DPICT: Deep Progressive Image Compression Using Trit-Planes,
CVPR22(16092-16101)
IEEE DOI 2210
Representation learning, Tensors, Image coding, Codecs, Quantization (signal), Scalability, Refining, Low-level vision BibRef

Wang, Z.W.[Zi-Wei], Xiao, H.[Han], Lu, J.W.[Ji-Wen], Zhou, J.[Jie],
Generalizable Mixed-Precision Quantization via Attribution Rank Preservation,
ICCV21(5271-5280)
IEEE DOI 2203
Degradation, Visualization, Quantization (signal), Costs, Codes, Complexity theory, Efficient training and inference methods, BibRef

Lee, D.H.[Dong-Hyun], Cho, M.[Minkyoung], Lee, S.W.[Seung-Won], Song, J.[Joonho], Choi, C.K.[Chang-Kyu],
A Novel Sensitivity Metric for Mixed-Precision Quantization With Synthetic Data Generation,
ICIP21(1294-1298)
IEEE DOI 2201
Measurement, Degradation, Sensitivity, Quantization (signal), Image coding, Perturbation methods, Neural networks, Deep Learning, Data Free BibRef

Chen, P.[Peng], Liu, J.[Jing], Zhuang, B.[Bohan], Tan, M.K.[Ming-Kui], Shen, C.H.[Chun-Hua],
AQD: Towards Accurate Quantized Object Detection,
CVPR21(104-113)
IEEE DOI 2111
Performance evaluation, Degradation, Quantization (signal), Image edge detection, Object detection, Detectors, Hardware BibRef

Liu, Y.[Yuang], Zhang, W.[Wei], Wang, J.[Jun],
Zero-shot Adversarial Quantization,
CVPR21(1512-1521)
IEEE DOI 2111
Training, Performance evaluation, Quantization (signal), Training data, Data models, Generators, Pattern recognition BibRef

Song, L.Y.[Liu-Yihan], Zhao, K.[Kang], Pan, P.[Pan], Liu, Y.[Yu], Zhang, Y.Y.[Ying-Ya], Xu, Y.H.[Ying-Hui], Jin, R.[Rong],
Communication Efficient SGD via Gradient Sampling with Bayes Prior,
CVPR21(12060-12069)
IEEE DOI 2111
Training, Degradation, Image coding, Quantization (signal), Object detection, Pattern recognition BibRef

Idelbayev, Y.[Yerlan], Molchanov, P.[Pavlo], Shen, M.[Maying], Yin, H.X.[Hong-Xu], Carreira-Perpiñán, M.Á.[Miguel Á.], Alvarez, J.M.[Jose M.],
Optimal Quantization using Scaled Codebook,
CVPR21(12090-12099)
IEEE DOI 2111
Quantization (signal), Image coding, Laplace equations, Transform coding, Pattern recognition, Image storage BibRef

Wu, Y.Y.[Yu-Yang], Qi, Z.Y.[Zhi-Yang], Zheng, H.M.[Hui-Ming], Tao, L.F.[Lv-Fang], Gao, W.[Wei],
Deep Image Compression with Latent Optimization and Piece-wise Quantization Approximation,
CLIC21(1926-1930)
IEEE DOI 2109
Image coding, Quantization (signal), Data compression, Pattern recognition, Task analysis BibRef

Pan, S.[Shi], Finlay, C.[Chris], Besenbruch, C.[Chri], Knottenbelt, W.[William],
Three Gaps for Quantisation in Learned Image Compression,
NTIRE21(720-726)
IEEE DOI 2109
Training, Visualization, Quantization (signal), Image coding, Neural networks BibRef

Sun, H., Cheng, Z., Takeuchi, M., Katto, J.,
End-To-End Learned Image Compression With Fixed Point Weight Quantization,
ICIP20(3359-3363)
IEEE DOI 2011
Quantization (signal), Image coding, Encoding, Tuning, Transform coding, Training, Gain, Image compression, neural networks, fine-tuning BibRef

Sözer, S.B., Koz, A., Akyüz, A.O., Zerman, E., Valenzise, G., Dufaux, F.,
Just Noticeable Quantization Levels For High Dynamic Range Images,
ICIP20(1201-1205)
IEEE DOI 2011
Quantization (signal), Dynamic range, Indexes, Discrete cosine transforms, Rendering (computer graphics), image rendering BibRef

Giudice, O., Allegra, D., Guarnera, F., Stanco, F., Battiato, S.,
Animated GIF Optimization By Adaptive Color Local Table Management,
ICIP20(843-847)
IEEE DOI 2011
Image color analysis, Indexes, Optimization, Color, Encoding, Image coding, Software, animated GIF, compression, optimization, color table BibRef

Christie, O., Rego, J., Jayasuriya, S.,
Analyzing Sensor Quantization of Raw Images For Visual SLAM,
ICIP20(246-250)
IEEE DOI 2011
Simultaneous localization and mapping, Quantization (signal), Visualization, Pipelines, Cameras, Image sensors, Visual SLAM, embedded computer vision BibRef

Xu, S.K.[Shou-Kai], Li, H.K.[Hao-Kun], Zhuang, B.[Bohan], Liu, J.[Jing], Cao, J.Z.[Jie-Zhang], Liang, C.R.[Chuang-Run], Tan, M.K.[Ming-Kui],
Generative Low-Bitwidth Data Free Quantization,
ECCV20(XII: 1-17).
Springer DOI 2010
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Nagel, M., Baalen, M.V., Blankevoort, T., Welling, M.,
Data-Free Quantization Through Weight Equalization and Bias Correction,
ICCV19(1325-1334)
IEEE DOI 2004
learning (artificial intelligence), neural nets, quantisation (signal), data-free quantization method, Adaptation models BibRef

Choi, Y., El-Khamy, M., Lee, J.,
Variable Rate Deep Image Compression With a Conditional Autoencoder,
ICCV19(3146-3154)
IEEE DOI 2004
codecs, data compression, image coding, learning (artificial intelligence), quantisation (signal), Codecs BibRef

Pahuja, A., Lucey, S.,
Lossy GIF Compression Using Deep Intrinsic Parameterization,
Preregister19(4581-4583)
IEEE DOI 2004
convolutional neural nets, data compression, image coding, image sequences, GIF hosting websites, Video Compression BibRef

Eghbali, S.[Sepehr], Tahvildari, L.[Ladan],
Deep Spherical Quantization for Image Search,
CVPR19(11682-11691).
IEEE DOI 2002
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Wang, K.[Kuan], Liu, Z.J.[Zhi-Jian], Lin, Y.J.[Yu-Jun], Lin, J.[Ji], Han, S.[Song],
HAQ: Hardware-Aware Automated Quantization With Mixed Precision,
CVPR19(8604-8612).
IEEE DOI 2002
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Bandoh, Y.[Yukihiro], Takamura, S.[Seishi], Shimizu, A.[Atsushi],
Complexity Reduction of Multi-Level DP Quantization Through Inter-Level Redundancy Elimination,
ICIP19(4075-4079)
IEEE DOI 1910
quantization, dynamic programming, bit-depth scalability, multi-layered structure BibRef

Cai, J., Zhang, L.,
Deep Image Compression with Iterative Non-Uniform Quantization,
ICIP18(451-455)
IEEE DOI 1809
Image coding, Quantization (signal), Training, Transform coding, Compressors, Decoding, Image reconstruction, Iterative Non-Uniform Quantization BibRef

Wang, Y.L.[Yi-Lin], Kum, S.U.[Sang-Uok], Chen, C.[Chao], Kokaram, A.[Anil],
A perceptual visibility metric for banding artifacts,
ICIP16(2067-2071)
IEEE DOI 1610
Coherence. Compressing low texture regions. BibRef

Froehlich, J., Su, G.M., Daly, S., Schilling, A., Eberhardt, B.,
Content aware quantization: Requantization of high dynamic range baseband signals based on visual masking by noise and texture,
ICIP16(884-888)
IEEE DOI 1610
Dynamic range BibRef

Zhang, T.[Ting], Qi, G.J.[Guo-Jun], Tang, J.H.[Jin-Hui], Wang, J.D.[Jing-Dong],
Sparse composite quantization,
CVPR15(4548-4556)
IEEE DOI 1510
BibRef

Babenko, A.[Artem], Lempitsky, V.[Victor],
Additive Quantization for Extreme Vector Compression,
CVPR14(931-938)
IEEE DOI 1409
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Makar, M.[Mina], Lakshman, H.[Haricharan], Chandrasekhar, V.[Vijay], Girod, B.[Bernd],
Gradient preserving quantization,
ICIP12(2505-2508).
IEEE DOI 1302
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Wang, Z., Simon, S., Klaiber, M., Ahmed, S., Richter, T.,
SSPQ: Spatial Domain Perceptual Image Codec Based on Subsampling and Perceptual Quantization,
ICIP12(1061-1064).
IEEE DOI 1302
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Yang, K.[Kai], Jiang, H.X.[Hong-Xu],
Optimized-SSIM Based Quantization in Optical Remote Sensing Image Compression,
ICIG11(117-122).
IEEE DOI 1109
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Motra, A.[Ajit], Thoma, H.[Herbert],
An adaptive LogLUV transform for High Dynamic Range video compression,
ICIP10(2061-2064).
IEEE DOI 1009
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Langton, J.T.[John T.], Prinz, A.A.[Astrid A.], Hickey, T.J.[Timothy J.],
Combining Pixelization and Dimensional Stacking,
ISVC06(II: 617-626).
Springer DOI 0611
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Sheinin, V., Jagmohan, A.,
Low Rate Uniform Scalar Quantization of Memoryless Gaussian Sources,
ICIP06(793-796).
IEEE DOI 0610
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Marques, O., Petljanski, B.,
A Novel Approach for Video Quantization Using the Spatiotemporal Frequency Characteristics of the Human Visual System,
BMVC05(xx-yy).
HTML Version. 0509
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Stentiford, F.,
A visual attention estimator applied to image subject enhancement and colour and grey level compression,
ICPR04(III: 638-641).
IEEE DOI 0409
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Imiya, A., Ito, A., Kenmochi, Y.,
Inverse quantization of digital binary images for resolution conversion,
ScaleSpace01(xx-yy). 0106
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Verscheure, O., and van den Branden Lambrecht, C.J.,
Adaptive Quantization Using a Perceptual Visibility Predictor,
ICIP97(I: 298-301).
IEEE DOI BibRef 9700

Tull, D.L., Safranek, R.J.,
Variable dimension quantization in the transform domain,
ICIP95(I: 302-305).
IEEE DOI 9510
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Adali, T., Wang, Y.[Yue],
Probabilistic neural networks for medical image quantification,
ICIP94(III: 889-892).
IEEE DOI 9411
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Valev, V.[Ventzeslav],
A model-based image quantization technique for supervised image recognition,
CAIP93(128-132).
Springer DOI 9309
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Linnainmaa, S.,
New efficient representation of photographic images with restricted number of gray levels,
ICPR88(I: 143-145).
IEEE DOI 8811
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Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Color Quantization of Images .