Computer Vision Seminar – Color Imaging

 
 

Course Homepage:
     http://cs.haifa.ac.il/hagit/courses/seminars/colorImaging/colorImaging.html
  
You must choose a topic. Your lecture must include at least 2 different papers (2 approaches or 2 problems etc) as well
as a paper on the topic using Machine Learning from the last 4 years which you can either cover in details or briefly.  

 

Please choose 3 topics that which you would like to present in order of preference.
List the Lecture number as well as the title.
I cant promise but will do my best to accommodate you.
Send your list to me at hagit@cs.haifa.ac.il with the subject: Seminar Topics

For fair play – send the email NOT BEFORE 21:00 Tonight 6/3/23
(Note: I will NOT read an email if arrived before 21:00).

 

Lecture 1 – Intro     (13/03/23)

Topics: The eye and retina, cone and rod sensors, image formation model, vector representation for color computation,

Resources: "Foundations of Vision" (book)  by Brian A. Wandell
 https://foundationsofvision.stanford.edu/

Chapter 3: The photoreceptor Mosaic
only up to  Visual Interferometry (not included)
Chapter 4: Wavelength Encoding
From: Photopic Wavelength Encoding up to and not including: A Standard Set of Color-Matching Functions
Also: The Biological Basis of Photopic Color-matching
Also: Cone Photopigments and Color-matching
Chapter 9: Color
Spectral Image Formation up to and not including Sensor-based error measures
then up to Color Constancy: Experiments
Note: before talking about Linear Models (of illumination) explain representation of spectral data by vectors and
talk about Linear Systems Methods in Chapter 2.

Also, can use slides from: IST01_Eye, IST02_Color, IST06_ImageFormation

Lecture 2– Color Spaces     (20/03/23)


Topics: linear color spaces, XYZ,  opponent color spaces: HSV, YIQ, YUV  
perceptual color spaces (non linear): LAB , SCIELAB

Resources: "Foundations of Vision" (book)  by Brian A. Wandell
 https://foundationsofvision.stanford.edu/
Chapter 9: color
The Perceptual Organization of Color up to and not including The Cortical Basis of Color Appearance

Find additional sources online

Also, can use slides from: IST03_ColorXYZ, IST04_ColorOpponent, IST05_ColorLAB

 

 

 

Lecture 3 Color Segmentation -   K-Means + Mean Shift () SKIP THIS TALK

 

Topics: Color Segmentation – intro and definition and examples.
             K-means segmentation , Mean shift segmentation

    

     Classic k-mean – Duda and Hart "Pattern Recognition"

Fast Image Segmentation Based on K-Means

Clustering with Histograms in HSV Color Space

Tse-Wei Chen 1, Yi-Ling Chen 2, Shao-Yi Chien

http://www.murase.nuie.nagoya-u.ac.jp/~twchen/paper/mmsp08_seg.pdf

 

Segmentation by Fusion of Histogram-Based

K-Means Clusters in Different Color Spaces

Max Mignotte

http://www.iro.umontreal.ca/~mignotte/Publications/IEEE_IP08.pdf

 

 

K-Means and EM

David Jacobs

http://www.cs.umd.edu/~djacobs/CMSC828seg/EM.pdf

 

D. Forsyth

http://luthuli.cs.uiuc.edu/~daf/courses/CS5432009/Week%206/EMSeg.pdf

 

Expectation-Maximization Algorithm and Image Segmentation

Daozheng Chen

http://www.cs.umd.edu/~dchen/papers/CMSC660_Term_Project.pdf

 



Mean Shift: A Robust Approach Toward Feature Space Analysis

D. Comaniciu and P. Meer

Pami 24(5) 2002

https://courses.csail.mit.edu/6.869/handouts/PAMIMeanshift.pdf

 

http://www.cse.yorku.ca/~kosta/CompVis_Notes/mean_shift.pdf

http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/TUZEL1/MeanShift.pdf

 

Improved mean shift segmentation approach for natural images

Yiping Hong , Jianqiang Yi and Dongbin Zhao .

(cant find online)

 

Mean Shift Based Clustering in High Dimensions: A Texture

Classification Example

Bogdan Georgescu, Ilan Shimshoni and Peter Meer

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.117.9132&rep=rep1&type=pdf

 

If there is time extend Mean shift to other applications:

D. Comaniciu, V. Ramesh, and P. Meer. Kernel-based object tracking. IEEE

Trans. Pattern Analysis Machine Intelligence, 25(5):564–577, May 2003.

Color image segmentation using mean shift and improved ant clustering

LIU Ling-xing, TAN Guan-zheng M. Sami Solima

http://edu.zndxzk.com.cn/down/upfile/soft/20120410/24-p1040-E110034.pdf

 

 


Lecture 4   Color Segmentation - Graph-Cut / Normalized Cut segmentation
(27/3/2023)   

 

       Topics: Images as graphs,  Min-cut, normalized-cut, Laplacian Matrix,
              multi-segment cuts, interactive segmentation (using GMM)

             


J. Shi and J.Malik,
“Normalized Cuts and Image Segmentation,” Proc. CVPR 1997.
also
IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888-905, August 2000.
http://www.cs.berkeley.edu/~malik/papers/SM-ncut.pdf

 

Boykov, Y., Jolly, M., " Interactive graph cuts for optimal boundary and region
segmentation of objects in N-D images." In: International Conference on Computer

Vision, Vancouver , BC. (2001) 105–112

http://www.csd.uwo.ca/faculty/yuri/Papers/iccv01.pdf

 

Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via

graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23 (2001) 1222–1239

http://www.csd.uwo.ca/faculty/yuri/Abstracts/pami01-abs.html

 

      Good slides on Graph rep

       http://web.cs.hacettepe.edu.tr/~erkut/bbm413.f13/w13-segmentation-b-4pp.pdf

 

 

Lecture 5 – Image Recoloring –        (17/4/2023)      

Topics to cover:   Colorization by Example, Using Similar Images 

Colorization by Example
Revital Irony, Daniel Cohen-Or, and Dani Lischinski
EGSR 2005
 http://www.cs.tau.ac.il/~dcor/online_papers/papers/colorization05.pdf

“Intrinsic colorization,”
X. Liu, L. Wan, Y. Qu, T.-T. Wong, S. Lin, C.-S. Leung, and P.-A. Heng,
ACM Transactions on Graphics (Proc. of SIGGRAPH Asia), vol. 27, no. 3, pp. 152 (1–9), 2008
http://www.cse.cuhk.edu.hk/~ttwong/papers/incolor/paper/incolor.pdf

Image Colorization Using Similar Images
Raj Kumar Gupta1
Alex Yong-Sang Chia2, Deepu Rajan, Ee Sin Ng2 and Huang Zhiyong
http://people.cs.clemson.edu/~jzwang/1301863/mm2012/p369-gupta.pdf

Automatic Image Colorization via Multimodal Predictions
Guillaume Charpiat , Matthias Hofmann , Bernhard Schölkopf
http://130.203.133.150/viewdoc/download?doi=10.1.1.165.8029&rep=rep1&type=pdf

Inducing Semantic Segmentation from an Example
Yaar SchnitmanYaron Caspi, Daniel Cohen-Or, and Dani Lischinski
ACCV2005
http://www.cs.tau.ac.il/~dcor/online_papers/papers/Yaar05.pdf

 

 

 


Lecture 6 
-  Color based Forgery detection - Chromatic Aberration     (1/5/2023) 

Topics to cover:   Forgery intro, Chromatic aberration explained,
           Forgery detection based on chromatic aberration 

A.C. Popescu  and H. Farid, ‘Exposing digital forgeries through chromatic aberration’,

ACM Multimedia and Security Workshop, pp. 48–55, 2006.

https://farid.berkeley.edu/downloads/publications/acm06c.pdf

 

T.V. Lanh, S. Emmanuel, and M.S. Kankanhalli,

‘Identifying source cell phone using chromatic aberration’,

IEEE International Conference on Multimedia and Expo, July 2007

 https://www.comp.nus.edu.sg/~mohan/papers/cellphone_forensics.pdf

 

I. Yerushalmy and H. Hel-Or,

‘Digital image forgery detection based on lens and sensor aberration’,

International Journal of Computer Vision, vol. 92, no. 1, pp. 71–91, 2011

https://www.researchgate.net/profile/Hagit_Hel-Or/publication/220659925_Digital_Image_Forgery_Detection_Based_on_Lens_and_Sensor_Aberration/links/573e126208aea45ee842e3e2/Digital-Image-Forgery-Detection-Based-on-Lens-and-Sensor-Aberration.pdf

 

Owen Mayer and Matthew C. Stamm

Accurate and Efficient Image Forgery Detection Using Lateral Chromatic Aberration

TIFS 2018

https://misl.ece.drexel.edu/wp-content/uploads/2018/02/Mayer_TIFS_2018.pdf

 

 

Lecture 7 – Shadow Removal from Images   (8/5/23)  

Topics to cover:   Intrinsic image, invariant color models, using anchor points,
          Shadow removal from Image Sequences

 

Eli Arbel and  Hagit Hel-Or
Texture-Preserving Shadow Removal in Color Images Containing Curved Surfaces
CVPR 2007

https://cs.haifa.ac.il/hagit/papers/CONF/CVPR07_ArbelHelOr-shadowRemoval.pdf

 

Graham D. Finlayson, Steven D. Hordley, and Mark S. Drew,
"Removing Shadows From Images using Retinex",
Color Imaging Conference, pp. 73-79, November 2002.
http://www.cs.sfu.ca/~mark/ftp/Cic10/cic10retinex.pdf
http://citeseer.ist.psu.edu/710866.html

 

G. D. Finlayson and S. D. Hordley, "Colour Constancy at a Pixel",
In Journal of Optical Society of America, A, Vol 18 (2), pp253-264, 2001.

https://colorincomputervision.com/html/wp-content/uploads/2012/09/ColorConstancy.pdf

 

G. D. Finlayson and S. D. Hordley, and M. S. Drew "
Recovery of chromaticity image free from shadows via illumination invariance",
In Proceedings International Conference on Computer Vision,
Workshop on Colour and Photometric Methods in Vision, 2003.
http://www.cs.sfu.ca/~mark/ftp/Iccv03ColorWkshp/iccv03wkshp.pdf

 

E. Salvador, P. Green, and T. Ebrahimi.

Shadow identification and classification using invariant color models.

In Proceedings of ICASSP 01, volume 3, pages 1545--1548. IEEE, 2001.

http://www.elec.qmul.ac.uk/staffinfo/andrea/papers/icassp01.pdf

 

Cast shadow segmentation using invariant color features

Elena Salvador,a Andrea Cavallaro,b,* and Touradj Ebrahimi

http://lts1pc19.epfl.ch/repository/Salvador2004_774.pdf

 

Color Invariance

J.M. Geusebroek, R. van den Boomgaard, A.W.M. Smeulders, H. Geerts

IEEE PAMI December 2001 (Vol. 23, No. 12)   pp. 1338-1350

http://csdl2.computer.org/persagen/DLAbsToc.jsp?resourcePath=/dl/trans/tp/&toc=comp/trans/tp/2001/12/iztoc.xml&DOI=10.1109/34.977559

 

Shadow removal from a real picture

International Conference on Computer Graphics and Interactive Techniques archive

 Masashi Baba Naoki Asada   

Proceedings of the SIGGRAPH 2003 conference on Sketches & applications

http://delivery.acm.org/10.1145/970000/965488/p1-baba.pdf?key1=965488&key2=8259480311&coll=GUIDE&dl=GUIDE&CFID=59009800&CFTOKEN=2645209

 

Shadow Removal from a Real Image Based on Shadow Density

Masashi Baba    Masayuki Mukunoki    Naoki Asada

http://www.cv.its.hiroshima-cu.ac.jp/~baba/Shadow/

 

Improving Shadow Suppression in Moving Object Detection with HSV Color Information

Rita Cucchiara, Costantino Grana, Massimo Piccardi, Andrea Prati, Stefano Sirotti

http://www.cs.ucsb.edu/~yfwang/courses/cs595/tomy/4%20shadow%20removal/Improving_shadow_suppresion_in_moving_object_detection_HSV_color_info_itsc_2001.pdf

 

Y. Weiss. Deriving intrinsic images from image sequences. In ICCV01, pages II:

68{75. IEEE, 2001.

http://www.cs.huji.ac.il/~yweiss/iccv01.pdf

Y. Matsushita and K. Nishino, “Illumination normalization with time-dependent intrinsic images

for video surveillance,” IEEE Trans. Pattern Anal. Mach. Intell., 26, pp. 1336-1348 (2004).

http://www.cvl.iis.u-tokyo.ac.jp/papers/all/650.pdf

Saritha Murali and V. K. Govindan and  Saidalavi Kalady

Single image shadow removal by optimization using non-shadow anchor values

Computational Visual Media volume 5, pages311–324(2019)

https://link.springer.com/content/pdf/10.1007/s41095-019-0148-x.pdf

 

 

Lecture 8 – Heart Rate from RGB   (15/5/23)

 

Remote heart rate measurement using low-cost RGB face video: a

technical literature review

Philipp V. ROUAST, Marc T.P. ADAM, Raymond CHIONG , David CORNFORTH

Frontiers in CS, 2016

https://www.rouast.com/pdf/rouast2016remote_a.pdf

 

Contactless and Hassle Free Real Time Heart Rate Measurement with Facial Video

Shubham Pratap Singh, Neeru Rathee1 Harsh Gupta, Paolo Zamboni, Ajay Vikram Singh

TSS-ISCU

https://d-nb.info/1170111076/34

 

Real Time Heart Rate Monitoring from Facial RGB Color Video Using Webcam

May 2016

Workshop of the Swedish Artificial Intelligence Society (SAIS 2016)At: Malmö

Hamidur Rahman, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk

https://ep.liu.se/ecp/129/002/ecp16129002.pdf

 

Dynamic heart rate measurements from video sequences

Yong-Poh Yu,1,* P. Raveendran,1,2 and Chern-Loon Lim1,3

Biomed Opt Express 2015

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4505702/

 

RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation

Xuesong Niu, Shiguang Shan, Hu Han, Member, Xilin Chen

https://arxiv.org/pdf/1910.11515.pdf

 

Heart Rate Measurement Using Facial Videos

Garima Uppal, Neelam Rup Prakash, Parveen Kalra

Advances in Computational Sciences and Technology Volume 10, Number 8 (2017)

https://www.ripublication.com/acst17/acstv10n8_14.pdf

 

 

Lecture 9 – Emotion detection from RGB sequences   (22/5/23)

 

Topics to cover:   what is emotion, Action Unites, micro emotions
       color spaces for face analysis, Micro expression detection from RGB sequences.

 

Facial color is an efficient mechanism to visually transmit emotion.

C. F. Benitez-Quiroz, R. Srinivasan, and A. M. Martinez.

Proceedings of the National Academy of Sciences,

115(14):3581–3586, 2018.

https://www.researchgate.net/publication/323864408_Facial_color_is_an_efficient_mechanism_to_visually_transmit_emotion/fulltext/5ab06548458515ecebeb1db9/Facial-color-is-an-efficient-mechanism-to-visually-transmit-emotion.pdf

 

Micro-expression recognition using color spaces. IEEE Transactions on Image Processing,

S.-J. Wang, W.-J. Yan, X. Li, G. Zhao, C.-G. Zhou, X. Fu, M. Yang, and J. Tao.

24(12):6034–6047, 2015.

https://www.researchgate.net/profile/Jianhua-Tao/publication/283479226_Micro-Expression_Recognition_Using_Color_Spaces/links/584c0cd808aeb989251f363c/Micro-Expression-Recognition-Using-Color-Spaces.pdf

 

Micro Expression classification using facial color and deep learning methods

Hadas Shahar and Hagit Hel
CVPR workshop – 2019

https://openaccess.thecvf.com/content_ICCVW_2019/papers/CVPM/Shahar_Micro_Expression_Classification_using_Facial_Color_and_Deep_Learning_Methods_ICCVW_2019_paper.pdf

 

 

Lecture   - Face Detection Using Color    ()  SKIP THIS TALK

 

Survey:
A Survey on Pixel-Based Skin Color Detection Techniques
Vladimir Vezhnevets 
Vassili Sazonov. Alla Andreeva
GRAFICON
http://graphicon.ru/oldgr/en/publications/text/gc2003vsa.pdf
 

A Robust Skin Color Based Face Detection Algorithm,
Sanjay Kr. Singh ,  D. S. Chauhan ,  Mayank Vatsa ,  Richa Singh
Journal of Science and Engineering, 2003
http://www.csee.wvu.edu/~mayankv/papers/tkjse.pdf

 

Robust Face Detection Combined Skin Color Features, Template Matching and VQ Histogram Method
Qiu Chen, Koji Kotani, Yoshiyuki Taniguchi, Zhibin Pan, and Tadahiro Ohmi
in Practical Environmentsh, Intelligent Automation and Soft Computing, Vol. 10, No. 2, pp. 143-154, 2004.
http://wacong.org/autosoft/vol10/10_2/6.pdf

 

Face Detection in Still Color Images Using Skin Color Information
Amine, Ghouzali and Rziza
Proceedings ISCCSP2006
http://www.eurasip.org/Proceedings/Ext/ISCCSP2006/defevent/papers/cr1268.pdf
 

Detecting human faces in color images
J. Cai ,  A. Goshtasby
Image and Vision Computing, 1999
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.122.9898&rep=rep1&type=url&i=0
http://www.cs.wright.edu/~agoshtas/facechroma.html

 

D. Chai and K. N. Ngan,
``Face Segmentation Using Skin-Color Map in Videophone Applications'',
IIEEE Trans. Circuits Sys. Video Tech., vol. 9, no. 4, pp. 551-564, June 1999
http://suraj.lums.edu.pk/~cs504m04/csvt99.pdf
 

Comparison of Five Color Models in Skin Pixel Classification
Benjamin D. Zarit ,  Boaz J. Super ,  Francis K. H. Quek
ICCV’99 Int’l Workshop on Color 1999
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.129.9666&rep=rep1&type=url&i=0

   

Lecture 10 – Demosaicing    (29/5/23)

 

Topics to cover:   Camera structure, sensors, what is Demosiacing. demosaicing artifacts
Demosaicing techniques.

Image Demosaicing: A Systematic Survey, Xin Li, Bahadir Gunturk, Lei Zhang, SPIE2011 https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.141.634&rep=rep1&type=pdf

Using Natural Image Properties as Demosaicing Hints. (ICIP 2004)
I. Omer and M. Werman
https://www.researchgate.net/publication/4138072_Using_natural_image_properties_as_demosaicing_hints

The Canonical Correlations of Color Images and their use for Demosaicing
Hel-Or, Yacov
HPL-2003-164R1
20040227
https://faculty.idc.ac.il/toky/Publications/TR/CCA.pdf

Comparison of color demosaicing methods
Olivier Losson, Ludovic Macaire, Yanqin Yang
https://hal.archives-ouvertes.fr/hal-00683233/document

Color Filter Array Demosaicking: New Method and Performance Measures,
Wenmiao Lu and Yap-Peng Tan, IEEE Transactions on Image Processing,
vol. 12, no. 10, pp. 1194-1210, Oct 2003.
http://elynxsdk.free.fr/ext-docs/Demosaicing/more/news0/Color%20Filter%20Array%20Demosaicking-New%20Method%20and%20Performance%20Measures.pdf



 

Lecture 11 - – Jpeg Compression and Extensions    (5/6/23)


Topics to cover:   Image compression general , JPEG compression, JPEG artifacts, JPEG 2000 compression

DCTune: A Technique for visual optimization of DCT quantization matrices for individual images (1993)
Andrew B. Watson, Society for Information Display Digest of Technical Papers XXIV, 946-949. http://vision.arc.nasa.gov/publications/sid93/sid93.pdf

Perceptual optimization of DCT color quantization matrices (1994)
Andrew B. Watson, Proceedings, IEEE International Conference on Image Processing, Austin, TX, IEEE Computer Society Press, pp. 100-104.
http://vision.arc.nasa.gov/publications/icip94.pdf

Perceptual adaptive JPEG coding (1996)
Ruth Rosenholtz & Andrew B. Watson Proceedings, IEEE International Conference on Image Processing, Lausanne, Switzerland, 1, 901-904.
http://vision.arc.nasa.gov/publications/AdaptiveJPEG/AdaptiveJPEG.pdf

 

 

Lecture 12 - Halftoning  (12/6/23) 

Topics to cover:   Printing process, what is halftoning, BW halftoning methods, Color halftoning

"Improving Digital Halftones by Exploiting Visual System Properties (summary and pointers to other papers)
http://vision.arc.nasa.gov/personnel/jbm/home/publications/proc/half.pdf

DBS
Principled halftoning based on human vision models (1992)
Jeffrey B. Mulligan & Albert J. Ahumada Jr., Proc. SPIE/SPSE Conference 1666 On Human Vision, Visual Processing, And Visual Display III, 109-121.
http://vision.arc.nasa.gov/personnel/jbm/home/publications/proc/huvi.pdf

temporal halftoning
Methods for Spatiotemporal Dithering. (1993)
Mulligan, J.B., SID Int. Symp. Dig. Tech. Papers, 24, 155-158.
http://vision.arc.nasa.gov/personnel/jbm/home/publications/proc/spie93.pdf

P. Hilgenberg, T.J. Flohr, C.B. Atkins, and C.A. Bouman. Least-squares modelbased video halftoning.
In Proceedings of the SPIE, volume 2179, pages 207--117, San Jose, 1994.
http://dynamo.ecn.purdue.edu/~bouman/publications/pdf/ei94a.ps

color halftoning
Digital halftoning methods for selectively partitioning error into achromatic and chromatic channels (1990)
Jeffrey B. Mulligan , Proc. SPIE/SPSE Conference No. 1249 On Human Vision, Visual Processing and Visual Display, Paper No. 21, Feb 1990.
http://vision.arc.nasa.gov/publications/spie90.pdf

Principled Methods for Color Dithering Based on Models of the Human Visual System. (1992)
Mulligan, J.B., and Ahumada, A.J. Jr., SID Int. Symp. Dig. Tech. Papers, 23, 194- 197.
http://vision.arc.nasa.gov/personnel/jbm/home/publications/proc/sid92.pdf

Color 1/2toning using DBS
T. J. Flohr, B. W. Kolpatzik, R. Balasubramanian,
D. A. Carrara, C. A. Bouman, and J. P. Allebach, "Model based color image quantization,"
Proc. of SPIE/IS&T Conf. on Human Vision, Visual Processing, and Digital Display, pp. 270-281, San Jose,
CA, January 31-February 4, 1993, Vol. 1993.
http://citeseer.ist.psu.edu/flohr93model.html

 

 

Lecture 13 – Image Quality Measures - () SKIP THIS TALK

 

Topics to cover:   What is IQA, types of noise, full, partial and no reference IQA

SSIM, SSIM for color, S-CIELAB color

Multi-scale structural similarity for image quality assessment
Z Wang, E P Simoncelli, A C Bovik.
37th Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA. November 9-12, 2003.
http://www.cns.nyu.edu/pub/eero/wang03b.pdf

Image quality assessment: From error visibility to structural similarity
Z Wang, A C Bovik, H R Sheikh, and E P Simoncelli.
IEEE Trans Image Processing, 13(4):600-612, Apr 2004.
http://www.cns.nyu.edu/pub/eero/wang03-reprint.pdf

Stimulus synthesis for efficient evaluation and refinement of perceptual image quality metrics
Z Wang and E P Simoncelli.
Proc. SPIE Conf on Human Vision and Electronic Imaging, San Jose, CA. January 2004.
http://www.cns.nyu.edu/pub/eero/wang04a.pdf


X. M. Zhang and B. A. Wandell,
"A spatial extension to cielab for digital color image reproduction,"
Society of Information Display Sumposium Technical Digest, vol. 27, pp. 731--734, 1996
http://citeseer.ist.psu.edu/zhang96spatial.html

http://citeseer.ist.psu.edu/cache/papers/cs/14589/http:zSzzSzwhite.stanford.eduzSzhtmlzSzxmeizSzscielabzSzscielab3zSzscielab3.pdf/zhang96spatial.pdf
http://white.stanford.edu/~brian/scielab/scielab3/scielab3.html

X. Zhang, D. A. Silverstein, J. E. Farrell, and B. A. Wandell, "Color image quality metric S-CIELAB and its application on halftone texture visibility," in COMPCON97 Digest of Papers, pp. 44--48, IEEE, 1997.
http://citeseer.ist.psu.edu/zhang97color.html
http://white.stanford.edu/~brian/scielab/spie97/spie97.html

X. Zhang, J. Farrell, B. A. Wandell, Applications of a spatial extension to CIELAB, in: SPIE, 1997.
http://citeseer.ist.psu.edu/zhang97applications.html

X. Zhang, B. A. Wandell: "Color image fidelity metrics evaluated using image distortion maps."
Signal Processing 70(3), 201--214, 1998.
http://citeseer.ist.psu.edu/zhang98color.html

Christian J. van den Branden Lambrecht, Joyce E. Farrell:
"Perceptual quality metric for digitally coded color images."
in Proceedings of the European Signal Processing Conference, pp. 1175--1178,
Trieste, Italy, September 10--13, 1996.
http://citeseer.ist.psu.edu/28332.html

Colour Image Quality Assessment Using the Combined Full-Reference Metric
Krzysztof Okarma
Computer Recognition Systems 4 pp 287-296

Full-Reference Image Quality Assessment Measure Based on Color Distortion
Zianou Ahmed Seghi and Fella Hachouf
IFIP Advances in Information and Communication Technology book series
https://www.researchgate.net/profile/Zianou-Seghir/publication/276291048_Full-Reference_Image_Quality_Assessment_Measure_Based_on_Color_Distortion/links/5556171708ae6943a87336dc/Full-Reference-Image-Quality-Assessment-Measure-Based-on-Color-Distortion.pdf

No-Reference Color Image Quality Assessment: From Entropy to Perceptual Quality
Xiaoqiao Chen, Qingyi Zhang, Manhui Lin, Guangyi Yang, Chu He
https://arxiv.org/pdf/1812.10695