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Image and Video Quality Assessment

Content

Nowadays, multimedia data are ubiquitous. You take pictures and videos with your camera and camcorders everyday. Have you thought about how you can judge the perceptual quality of the pictures and videos you made and how good your camera/camcorder is? In this seminar, we will discuss algorithms for image and video quality assessment, especially for the purpose of assessing performance of digital cameras and camcorders. The following aspects of perceptual quality are of interests: blur/sharpness, noise, color fidelity, resolution, geometric distortions, blockiness, ringing, frame dropping and freezing, and so on.

Schedule

Mo. 17:00 - 18:30 Z 613

First meeting October 20, 2014


Date Title Speaker Slides
20.10.14 Introduction to the seminar and organizational matters Dietmar Saupe  pdf
27.10.14 Introduction the LaTeX Beamer package for presentation slides Juan Quintana  pdf
03.11.14 Introduction to image quality assessment I Igor Zingman  pdf
10.11.14 Soft skills: How to present a paper/topic Dietmar Saupe see below
17.11.14 Introduction to image quality assessment II  Maciej Gratkowski  pdf
24.11.14 How to model perceptual visual quality Roman Byshko  pdf
08.12.14 Perceptual quality aware image coding Alex Artiga Gonzales  pdf
15.12.14 Subjective video quality assessment Nayeem Khan  pdf
12.01.15 VQA with SSIM Waqar Detho  pdf
  Natural scene statistics Muhammad Waseem  pdf
19.01.15 Reduced reference VQA Imran Mehmood  pdf
  Blurriness estimation Muhammad Faizan  pdf
26.01.15 - canceled - -  pdf
02.02.15 IQA/VQA with saliency maps Qaiser Jamal  pdf
  Bit stream based NR-VQA Masud Rana  pdf


References

Introduction the LaTeX Beamer package for presentation slides

How to make a presentation with Latex - Introduction to Beamer, Math-Linux.com.

LaTeX/Presentations, Wikibooks.

Introduction to image quality assessment I

Z. Wang, A. C. Bovik, and L. Lu. Why is image quality assessment so difficult? In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Vol. 4. IEEE. 2002, pp. IV3313.

Wang, Zhou, and Alan C. Bovik. Mean squared error: love it or leave it? A new look at signal fidelity measures. Signal Processing Magazine, IEEE 26.1 (2009): 98-117.

Introduction to image quality assessment II

Winkler, Stefan. Digital video quality: vision models and metrics. Chapter 2. John Wiley & Sons, 2005. (Use your Uni-KN e-mail login)

Soft skills: How to present a paper and how to write a seminar report

S. P. Jones, Microsoft Research,  How to write a great research paper, Research skills module, video, Cambridge University, UK, 2012.

T. Kovacs, P. Flach, Written Communication, Critical Assessment, and Avoiding Plagiarism, Learning resources, University of Bristol, UK.

U. Waldmann, How to give seminar talks, MPI Saarbrücken, 2008.

Guidance Notes on Plagiarism, School of Computer Science, University of Birmingham, UK.

William Strunk, Jr., Elements of Style, 1920. Online version.

Donald E. Knuth, Tracy Larrabee, and Paul M. Roberts, Mathematical writing, §1 Minicourse on technical writing. Lecture notes for CS 209, Stanford University, 1987.

How to model perceptual visual quality

W. Lin and C.-C. Jay Kuo. Perceptual visual quality metrics: A survey. In: Journal of Visual Communication and Image Representation 22.4 (2011), pp. 297312.

Engelke, Ulrich, and H-J. Zepernick. Perceptual-based quality metrics for image and video services: A survey. Next Generation Internet Networks, 3rd EuroNGI Conference on. IEEE, 2007.

Perceptual quality aware image coding

D. M. Tan, C. Tan, and H. R. Wu. Perceptual color image coding with JPEG2000. In: IEEE Transactions on Image Processing 19.2 (2010), pp. 374--383.  (Use your Uni-KN e-mail login)

Eckert, Michael P., and Andrew P. Bradley. Perceptual quality metrics applied to still image compression. Signal processing 70.3 (1998) 177--200. (Use your Uni-KN e-mail login)

These papers are not online and but can be provided individually.

SSIM

Z. Wang, E. P. Simoncelli, and A. C. Bovik. Multi-scale Structural Similarity for Image Quality Assessment. In: IEEE Asilomar Conference on Signals, Systems and Computers. Nov. 2003.

Wang, Zhou, et al. Image quality assessment: from error visibility to structural similarity. Image Processing, IEEE Transactions on 13.4 (2004): 600-612.

A MatLab implementation of the proposed algorithm is available online at http://www.cns.nyu.edu/~lcv/ssim/. The presentation should include design and results of a set of experiments based on the code and selected images. 

Subjective video quality assessment

Video Quality Expert Group, Motivation, Objectives and Rules, Version 1.0 (Poland VQEG meeting), 2010. 

Recommendation ITU-R BT.500-11 - Methodology for the subjective assessment of the quality of television pictures. 2002. 

Full reference IQA/VQA based on the SSIM

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing, vol. 13, pp. 600-612, Apr. 2004.

Z. Wang, E. P. Simoncelli, and A. C. Bovik. Multi-scale Structural Similarity for Image Quality Assessment. In: IEEE Asilomar Conference on Signals, Systems and Computers. Nov. 2003.

Z. Wang, L. Lu, and A. C. Bovik, Video quality assessment based on structural distortion measurement, Signal Processing: Image Communication 19, pp. 121--132, Feb. 2004.

Natural scene statistics

A. K. Moorthy and A. C. Bovik, Blind image quality assessment: From natural scene statistics to perceptual quality, IEEE Transactions on Image Processing, vol. 20, pp. 3350-3364, Dec. 2011.

M. A. Saad, A. C. Bovik, and C. Charrier, Blind image quality assessment: A natural scene statistics approach in the DCT domain, IEEE Transactions on Image Processing, vol. 21, pp. 3339- 3352, Aug. 2012.

Reduced reference VQA

M. G. Martini, B. Villarini, and F. Fiorucci, A reduced-reference perceptual image and video quality metric based on edge preservation, EURASIP J. Adv. Sig. Proc., vol. 2012, p. 66, 2012.

A. Rehman and Z. Wang, Reduced-reference image quality assessment by structural similarity estimation, IEEE Transactions on Image Processing, vol. 21, no. 8, pp. 3378--3389, 2012.

J. Redi, P. Gastaldo, I. Heynderickx, and R. Zunino, Color distribution information for the reduced-reference assessment of perceived image quality, IEEE Trans. Circuits Syst. Video Techn., vol. 20, no. 12, pp. 1757--1769, 2010. 

Blurriness estimation

A. Ciancio, A. L. N. Targino da Costa, E. A. B. da Silva, A. Said, R. Samadani, and P. Obrador, No-reference blur assessment of digital pictures based on multifeature classifiers, IEEE Transactions on Image Processing, vol. 20, pp. 64-75, Jan. 2011.

C. T. Vu, T. D. Phan, and D. M. Chandler, S-3: A spectral and spatial measure of local perceived sharpness in natural images, IEEE Transactions on Image Processing, vol. 21, pp. 934-945, Mar. 2012.

Blockiness (topic not yet assigned)

S. Liu and A. C. Bovik, Efficient DCT-domain blind measurement and reduction of blocking artifacts, IEEE Transactions on Circuits and Systems for Video Technology, vol. 12, pp. 1139-1149, Dec. 2002.

G. Zhai, W. Zhang, X. Yang, W. Lin, and Y. Xu, No-reference noticeable blockiness estimation in images, Signal Processing: Image Communication, vol. 23, pp. 417-432, July 2008.

Visual attention modeling

A. Borji and L. Itti. State-of-the-art in visual attention modeling. In: IEEE Transactions on Pattern Analysis and Machine Intelligence 35.1 (2013), pp. 185--207.

Itti, Laurent, Christof Koch, and Ernst Niebur. A model of saliency-based visual attention for rapid scene analysisIEEE Transactions on pattern analysis and machine intelligence 20.11 (1998): 1254-1259.

Stas Goferman, Lihi Zelnik-Manor, Ayellet Tal, Context-Aware Saliency Detection, PAMI 2012.

IQA/VQA with saliency maps

H. Boujut, J. Benois-Pineau, T. Ahmed, O. Hadar, and P. Bonnet, A metric for no-reference
video quality assessment for HD TV delivery based on saliency maps, in IEEE International
Conference on Multimedia and Expo, (Barcelona, Spain), July 2011.

H. Liu and I. Heynderickx, Visual attention in objective image quality assessment: Based on eye-tracking data, IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, pp. 971--982, July 2011.

D. Culibrk, M. Mirkovic, V. Zlokolica, M. Pokric, V. Crnojevic, and D. Kukolj, Salient motion features for video quality assessment, IEEE Transactions on Image Processing, vol. 20, pp. 948--958, Apr. 2011.

H. Boujut, J. Benois-Pineau, T. Ahmed, O. Hadar, and P. Bonnet, No-reference video quality assessment of H.264 video streams based on semantic saliency maps, in Image Quality and System Performance IX, Proceedings of SPIE, (Burlingame, CA, USA), Jan. 2012.

Bit stream based NR-VQA

F. Yang, S. Wan, Q. Xie, and H. R. Wu, No-reference quality assessment for networked video via primary analysis of bit stream, IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, pp. 1544-1554, Nov. 2010.

Brandão, Tomás, and Maria Paula Queluz. "No-reference quality assessment of H. 264/AVC encoded video." Circuits and Systems for Video Technology, IEEE Transactions on 20.11 (2010): 1437-1447.