overview of quality assessment for visual signals and newly emerged trends 1 king n. ngan †, and...

Overview of Quality Assessment for Visual Signals and Newly Emerged Trends

1

King N. Ngan†, and Lin Ma†‡

†Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong‡Lenovo Corporate Research Hong Kong Branch, Hong Kong

Outline

• Introduction• Quality assessment for traditional visual

signals• Quality assessment for newly emerged visual

signals• Conclusions

2

Introduction

• Most of information is represented in the form of digital visual signals, such as images, videos, etc.

• Products (phone cameras) and services (YouTube) based upon visual signals have grown at an exponential rate.

• Quality assessment of visual signals are very important.

3

Introduction

• Quality assessment: shape the full spectrum of technology development and enable new applications– Visual signal acquisition– Compression– Processing– Transmission

4

Introduction

• Quality assessment– Subjective measurement

• Benefits– Most reliable

• Drawbacks– Time-consuming, laborious, and expensive– Infeasible for on-line or real-time signal manipulations– Depend on the assessor’s physical conditions, emotion, and so on.

• Subjective measurement is employed to build the visual signal subjective database.

5

Introduction• Subjective

quality database– Image: LIVE,

A57, IRCCy/IVC, MICT, TID2008, and so on.

– Video: LIVE, IVP, MMSP ， NYU, VQEG, and so on.

6

1. VQEG. (2000) Final Report From the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment. [online] Available: http://www.vqeg.org.

2. H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms”, IEEE Trans. Image Process., Vol. 15, pp. 3441-3452, 2006.

3. S. Winkler, “Analysis of public image and video database for quality assessment”, IEEE Journal of Selected Topics in Signal Processing, vol. 6, no. 6, pp. 616-625, Oct. 2012

Introduction

• Objective measurement– Build computational model

• Visual signal as input, and output a number to indicate the perceptual quality.

• Quality metrics for traditional visual signals have been extensively researched1.

71. W. Lin, and J. C. C. Kuo, “Perceptual visual quality metrics: a survey”, Journal of Visual Communication and Image

Representation, vol. 22, no. 4, pp. 297-312, 2011.

Introduction

• New device/service development newly emerged visual signals– Scalable and mobile video– High dynamic range (HDR) images– Image segmentation– 3D image/video– Retargeted image/video

• New challenges for designing quality metric of newly emerged visual signals.

8

Outline




9

Quality assessment for traditional visual signals

• Quality metrics can be roughly categorized:– Full-reference: original signal is available

• Watermarking• Compression

– Reduced-reference: partial information is available• Quality monitoring• Error concealment

– No-reference: only distorted visual signal• Denoising• Enhancement

10

Full-Reference Quality Metric

• Original visual signal is available– Mean square error (MSE) and peak signal to noise ratio (PSNR)– Structural similarity index (SSIM)1

– Visual information fidelity (VIF)2

– Feature similarity index (FSIM)3

– Internal generative mechanism (IGM)4

• New trends:– Impairment decoupling approach– Machine learning approach

11

1. Z. Wang, A. C. Boviik, H. R. Sherkh, et al. “Image quality assessment: from error visibility to structural similarity”, TIP, vol. 13, no. 4, pp. 600-612, 2004.

2. H. R. Sherkh, and A. C. Bovik, “Image information and visual quality”, TIP, vol. 15, no. 2, pp. 430-444, 2006.3. L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: A Feature Similarity Index for Image Quality Assessment,” TIP, vol. 20, no.

8, pp. 2378-2386, 2011.4. J. Wu, W. Lin, G. Shi, et al. “A perceptual quality metric with internal generative mechanism”. TIP, vol. 22, no. 1, pp. 43-54,

2013.

Impairment Decoupling Approach

• Impairment decoupling1,2,3 – Detail losses– Additive impairments

• Different distortion are treated separately• Different distortion correlates with HVS

perception differently.

12

1. Songnan Li, Lin Ma, and King N. Ngan, "Full-reference Video Quality Assessment by Decoupling Detail Losses and Additive Impairments", TCSVT, vol. 22, no. 7, pp. 1100-1112, Jul. 2012.

2. Songnan Li, Fan Zhang, Lin Ma, and King N. Ngan, "Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments", TMM. vol. 13, no. 5, pp. 935-949, Oct. 2011.

3. Songnan Li, Lin Ma, and King N. Ngan, "Video Quality Assessment by Decoupling Additive Impairments and Detail losses", QoMEX 2011.

Impairment Decoupling Approach

13

(a) Separating the test image into the original image and the difference image.

(b) Separating the difference image into the detail loss image and the additive impairment image.

(c) Separating the test image into the restored and the additive impairment image.

1. Songnan Li, Lin Ma, and King N. Ngan, "Full-reference Video Quality Assessment by Decoupling Detail Losses and Additive Impairments", TCSVT, vol. 22, no. 7, pp. 1100-1112, Jul. 2012.

Machine Learning Approach

• Machine learning approach1,2,3

• Features extraction– Original image– Distorted image

• Feature comparison– Machine learning approach

• Learn complex data pattern• Model complex HVS property

14

1. M. Narwaria, W. Lin, A. E. Cetin, “Scalable image quality assessment with 2D mel-cepstrum and machine learning approach”, Pattern Recognition, vol. 45, no. 1. pp. 299-313, 2011.

2. M. Narwaria, W. Lin, “Objective image quality assessment based on support vector regression”, TNN, vol. 21, no. 3, pp. 515-519, 2010.

3. T. J. Liu, W. Lin, J. C. C. Kuo, “A multi-metric fusion approach to visual quality assessment”, QoMEX, 2011.

Reduced-Reference Quality Metric• Distortion modeling1,2

– Modeling the distortion behavior• HVS property modeling3,4

– HVS related features for quality assessment• Natural image/video statistics5,6,7

– Statistics variation can represent the distortion level.

15

1. S. Wolf, and M. Pinson, “Low bandwidth reduced reference video quality monitoring system”, VPQM, 2005.2. M. Tagliasacchi, G. Valenzise, M. Naccari, “Reduced-reference structural similarity approximation for videos corrupted by channel errors”, Multimedia

Tools Applications, vol. 48, no. 3, pp. 471-492, 2010.3. P. Le Callet, C. Viard-Gaudin, D. Barba, “A convolutional neural network approach for objective video quality assessment”, TNN, vol. 17, no. 5, pp.

1316-1327, 2006.4. M. H. Pinson, S. Wolf, “A new standardized method for objectively measuring video quality”, IEEE transactions on Broadcasting, vol. 50, no. 3, pp. 312-

322, 2004.5. Lin Ma, Songnan Li, and King N. Ngan, "Reduced-Reference Video Quality Assessment of Compressed Video Sequences“, TCSVT , vol. 22, no. 10,

pp. 1441-1456, Oct. 2012.6. Lin Ma, Songnan Li, Fan Zhang, and King N. Ngan, "Reduced-Reference Image Quality Assessment Using Reorganized DCT-Based Image

Representation", TMM, vol. 13, no. 4, pp. 824-829, Aug. 2011.7. Lin Ma, Songnan Li, and King N. Ngan, "Reduced-Reference Image Quality Assessment in Reorganized DCT Domain", Signal Processing: Image

Communication.

Reduced-Reference Quality Metric

• Sender side: feature extraction and representation– DCT coefficient reorganization – GGD modeling the DCT coefficient distribution– RR parameters are represented by 162 bits.

• Receiver side: quality analysis– Histogram building for each reorganized DCT subband– City block distance (CBD) measures the difference between original and

distorted image.

16

R eference Im age

Sender S ide

R eceiver S ide

D istorted Im age R eorganization D C T

G G D M odeling

H istogram B uilding

R eorganization D C T

),( kd

kkcity ppd

C ity-block D istancefor Each Subband

K

k

kd

kkcitydist ppd

cV

110 |),(|

11log

V isual Q uality of Im age

1. Lin Ma, Songnan Li, Fan Zhang, and King N. Ngan, "Reduced-Reference Image Quality Assessment Using Reorganized DCT-Based Image Representation", TMM, vol. 13, no. 4, pp. 824-829, Aug. 2011.

No-Reference Quality Metric

• Specific distortion– JPEG1

– JPEG 20002

– Blurring3

• Generic NR metrics4,5,6

– Different NR metrics designed for specific distortions are fused together

17

1. L. Liang, S. Wang, J. Chen, et al. “No-reference perceptual image quality metric using gradient profiles for JPEG 2000”, SPIC, vol. 25, no. 7, pp. 502-516, 2010.

2. T. brandao, M. P. Queluz, “No-reference image quality assessment based on DCT domain statistics”, Signal Processing, vol. 88, no. 4, pp. 822-833, 2008.

3. R. Ferzli, L. Karam, “No-reference objective image sharpness metric based on the notion of just noticeable blur”, TIP, vol. 18, no. 4, pp. 717-728, 2009.4. A. Mittal, G. S. Muralidhar, J. Ghosh, et al. “Blind image quality assessment without human training using latent quality factors”, SPL, vol. 19, no. 2, pp.

75-78, 2012.5. M. A. Saad, A. C. Bovik, et al., “A perceptual DCT statistics based blind image quality metric”, SPL, vol. 17, no. 6, pp. 583-586, 2010.6. A. K. Moorthy, A. C. Bovik, “Blind image quality assessment: from natural scene statistics to perceptual quality”, TIP, vol. 20, no. 12, pp. 3350-3364,

2011.

Quality assessment for traditional visual signals

• Experimental results– Statistical parameters1,2: LCC, SROCC, and RMSE

18

1. VQEG. (2000) Final Report From the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment. [online] Available: http://www.vqeg.org.

2. H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms”, IEEE Trans. Image Process., Vol. 15, pp. 3441-3452, 2006.

Comparison of different image quality metrics

Comparison of different video quality metrics

Outline




19

Scalable and Mobile Video Quality Assessment

• Scalable video quality assessment– HD video needs to be displayed in different

resolutions.– Scalable video coding (SVC) for content

distribution1

• Video codec, content, spatial resolution, temporal resolution, and frame quality.

– Public SVC video database1 was created by a pair-wise comparison.

201. J. S. Lee, et al., “Subjective evaluation of scalable video coding for content distribution”, ACM MM, 2010.


• Mobile video quality assessment– Smartphones and tablets require more and more

mobile videos.– Perceptual quality assessment of mobile videos

are needed.– A public database1 is created on mobile phones

and tablets.

21

1. A. K. Moorthy, et al., “Video quality assessment on mobile devices: subjective, behavioral and objective studies”, J-STSP, vol. 6, no. 6, pp. 652-671, Oct. 2012.


• Mobile video quality assessment1

– PSNR, SSIM, VIF, VQM, MOVIE are not suitable for evaluating mobile videos.

– Video content, bit rate, frame resolution, frame rate needs to be further considered.

22

Quality metric comparisons on the mobile database1

1. A. K. Moorthy, et al., “Video quality assessment on mobile devices: subjective, behavioral and objective studies”, J-STSP, vol. 6, no. 6, pp. 652-671, Oct. 2012.

1. H. Yeganeh, and Z. Wang, “Objective quality assessment of tone mapped images”, TIP, accepted. 2. H. Yeganeh, and Z. Wang, “Objective quality assessment of tone mapping algorithms”, ICIP, 2010.

High Dynamic Range Image Quality Assessment

• High dynamic range (HDR) image quality– Tone mapped image quality assessment1,2

• Structural fidelity:– Slocal compares the LDR and HDR image patch

• Statistical naturalness– Histograms of mean and standard deviation can be well fitted

using a Gaussian Pm and Beta probability Pd .

23

1. M. Cadik, O. Hajdok, A. Lejsek, et al., “Evaluation of tone mapping operators”, http://dcgi.felk.cvut.cz/home/cadikm/tmo/.

High Dynamic Rang Image Quality Assessment

• Quality model

• Validation on subject–rated image database1

• It can be observed that naturalness of image is not well depicted, while structural fidelity performs more reliably.

24

Image Segmentation Quality Assessment

• Image segmentation quality assessment– common characteristics or semantic information

of the objects (e.g., homogeneous regions, smooth boundaries, etc.),

– empirical measures on the uniformity of colors1,2

– luminance3

– the shape of object regions4

25

1. J. Liu, and Y. Yang, “Multiresolution color image segmentation”. IEEE Trans. Pattern Analysis and Machine Intelligence. vol. 16, pp. 689-700, 1994.

2. M. Borsotti, and P.C.Schettini, “Quantitative evaluation of color image segmentation results”, Pattern Recognition Letter, vol. 19, pp. 741-747, 1998.

3. H. Zhang, J. Fritts, and S. Goldman, “An entropy-based objective segmentation evaluation method for image segmentation”. SPIE Electronic Imaging Storage and Retrieval Methods and Applications for Multimedia, pp.38-49, 2004.

4. X. Ren, and M. Jitendra, “Learning a classification model for segmentation”, Proc. ICCV, pp. 10-17, 2003.

26

External Region

External Region

Object Region

True Object

Ground Truth


Quantity

)()(

)(1

1

regionexternalregionobject

regionexternalquantity

RNumRArea

RAreaS

27


2area

pre recS

pre rec

Harmonic mean

( )

( )true object

object region

Area Rpre

Area R

( )

( )true object

ground truth

Area Rrec

Area R

Area

pre = precision; rec = recall

28


( )( )G

G

G G

refdisf x

refdis D x

G Grefdis k length

x I,

External Contour

contour topixel of distanceshortest )(

box; bounding of diagonal distance; reference

xD

lengthrefdis

G

GG

29


External Contour

min( ( ), ( ))

max( ( ), ( ))

G S

G S

xcontour

x

f x f xS

f x f x

object region area contourS S S


Content

2.08 1.04

( )

( )true object

object areaground truth

Area RS

Area R

content object area object textureS S S

( )

( )object texture

Sobel xS

Sobel y

true objectx R ground truthy R

,


Overall Metric

overall quantity object region contentS S S S


Experimental Results

[1, 2]Jaccard

Index [3]Proposed

(without )Proposed

76 images by Achanta

SROCC 0.74 0.80 0.82 0.83

LCC 0.70 0.79 0.81 0.84

76 images by Rahtu

SROCC 0.84 0.85 0.88 0.89

LCC 0.85 0.89 0.89 0.91

Overall 152 images

SROCC 0.81 0.84 0.87 0.89

LCC 0.82 0.84 0.85 0.88

SROCC: Spearman Rank-Order Correlation Coefficients LCC: Linear Correlation Coefficient

1. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, “Frequency-tuned salient region detection”, Proc. IEEE CVPR, pp. 1597-1604, Miami, USA, Jun. 2009.2. E. Rahtu, J. Kannala, M. Salo, and J. Heikkila, “Segmenting salient objects from images and videos”, Proc. ECCV, pp. 366-379, Crete, Greece, Sep. 2010. 3. F. Ge, S. Wang and T. Liu, “New benchmark for image segmentation evaluation”, Journal of Electronic Imaging, vol.16,no.3, 033011, Jul-Sep. 2007.

F measure

contentS

1. A. K. Moorthy, C. Su, A. Mittal, et al. LIVE 3D Image Quality Database.2. A. Benoit, P. Le Callet, P. Campisi, R. Cousseau. IRCCyN/IVC 3D Image Quality Database.3. L. Goldmann, F. De Simone, T. Ebrahimi, MMSP 3D Image Quality Assessment Database.4. L. Goldmann, F. De Simone, T. Ebrahimi, MMSP 3D Video Quality Assessment Database.

3D Image/Video Quality Assessment

• Stereoscopic/3D image and video quality assessment is becoming more and more important.

• Many 3D image/video subjective quality databases have been created1,2,3,4.

• Depth information should be considered and has already been incorporated in the database1.

33


• 3D image/video quality assessment– 3D image database without considering depth

information

341. A. K. Moorthy, C. C. Su, A. Mittal and A. C. Bovik, “Subjective evaluation of stereoscopic image quality,” SPIC, 2013, accepted.2. A. K. Moorthy, A. C. Bovik, “A two-step framework for constructing blind image quality indices”, SPL, vol. 17, no. 2, pp. 287-599, 2011.


• 3D image/video quality assessment– 3D image database with depth information1

35

1. A. K. Moorthy, C. C. Su, A. Mittal and A. C. Bovik, “Subjective evaluation of stereoscopic image quality,” SPIC, 2013, accepted.2. A. Benoit, P. Le Callet, P. Campisi, and R. Cousseau, “Quality assessment of stereoscopic images”, EURASIP Journal on Image and Video

Processing, pp. 1–13, 2009. 3. C.T.E.R. Hewage, and M.G. Martini, “Reduced-reference quality metric for 3D depth map transmission”, Proc. ICIP, 2010. 4. J. You, L. Xing, A. Perkis, and X.Wang, “Perceptual quality assessment for stereoscopic images based on 2D image quality metrics and

disparity analysis”, Proceedings of the International Workshop on Video Processing and Quality Metrics, 2010.


• 2D IQA performs well in terms of correlation with human subjectivity.

• Considering disparity/depth in 3D quality metric cannot materially improve the performance.– 3D metric is simply extension of 2D metric.– Incorporating depth/disparity is not based on any

perceptual principles.

36


• Depth information is important– Displaying– Rendering– Interaction– Segmentation

• 3D image/video quality assessment– Depth image-based rendering (DIBR)1 based synthesized view

evaluation problem. Different view synthesis algorithms are evaluated.

– RR quality metric for 3-D videos2

• depth edges and color images in the areas in the proximity of edges are extracted for the RR quality metric

37

1. E. Bosc, R. Pepion, P. Le Callet, M. Koppel, P. Mdjiki-Nya, M. Pressigout, and L. Morin, “Towards a new quality metric for 3-D synthesized view assessment”, IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 7, pp. 1332-1343, Nov. 2011.

2. C. T. E. R. Hewage, and M. G. Martini, “Reduced-reference quality assessment for 3D video compression and transmission”, IEEE Trans. Consumer Electronics, vol. 57, no. 3, pp. 1185-1193, Aug. 2011.

1. M. Rubinstein, et al., “A comparative study of image retargeting”, in Proc. SIGGRAPH Asia 2010.2. L. Ma, W. Lin, C. Deng, and K. N. Ngan, “Image retargeting quality assessment: a study of subjective scores and objective metrics”, IEEE

Journal of Selected Topics in Signal Processing, vol. 6, no. 6, pp. 626-639, Oct. 2012.

Retargeted Image Quality Assessment

• Two image retargeting database– MIT database: aim at a comparative study of

existing retargeting methods1

– CUHK database: aim at quality comparisons of retargeted images2

38


Source image– Frequently encountered attributes

• Face and people• Clear foreground object• Natural scenery (containing smooth or texture region)• Geometric structure (evident lines or edges)

39

1. A. Shamir et al., “Seam-carving for media retargeting”, Communication of the ACM, vol. 52, no. 1, pp. 77-85, Jan. 2009.2. W. Dong et al., “Optimized image resizing using seam carving and scaling”, in Proc. SIGGRAPH 2009.3. L. Wolf, M. Guttmann, D. Cohen-Or, “Non-homogeneous content-driven video-retargeting”, in Proc. ICCV 2007.4. Y. Wang, C. Tai, O. Sorkine, and T. Lee, “Optimized scale-and-stretch for image resizing”, in Proc. SIGGRAPH Asia 2008.5. Y. Pritch, E. Kav-Venaki, and S. Peleg, “Shift-map image editing”, in Proc. ICCV 2009.6. M. Rubinstein, A. Shamir, and S. Avidan, “Multi-operator media retargeting”, in Proc. SIGGRAPH 2009.7. Z. Karni et al., “Energy-based image deformation”, in Proc. Symposium on Geometry Processing 2009.8. P. Krahenbuhl et al., “A system for retargeting of streaming video”, in Proc. SIGGRAPH Asia 2009.

Retargeted Image Quality Assessment• Retargeting methods

– Crop– Scale– Seam carving 1

– Optimized seam carving and scale2

– Non-homogeneous retargeting3

– Scale and stretch4

– Shift-map editing5

– Multi-operator6

– Energy-based deformation7

– Streaming video8

• Retargeting is restricted in only one dimension.• Two resizing scales (the source image is shrunk to 75% and 50%)

40

1. ITU-R Recommendation BT.500-11, “Methodology for the subjective assessment of the quality of television pictures”, International Telecommunications Union, Tech. Rep., 2000.

2. Video Quality Experts Group. Available at http://www.its.bldrdoc.gov/vqeg/


• Subjective Testing– Simultaneous double stimulus for continuous

evaluation (SDSCE)1

• Source image should be presented to the viewers

– Absolute category rating scale2

• 5-discrete category rating

– User interface– On-line subjective testing

• http://137.189.32.220:8080/war/

41

http://www.its.bldrdoc.gov/vqeg/

http://137.189.32.220:8080/war/


• Subjective Testing– Viewers have normal vision (with or without corrective

glasses).– Two sessions are employed to reduce the effect of

viewer fatigue.– First session

• 15 viewers are experts• 15 viewers are non-experts

– Second session• 18 viewers are experts• 16 viewers are non-experts

42


• Subjective score processing1,2

43

The horizontal axis corresponds to the image number and the vertical axis corresponds to the MOS value. The blue star indicates the obtained MOS value. And the red error bar indicates the standard deviation of the subjective scores

1. ITU-R Recommendation BT.500-11, “Methodology for the subjective assessment of the quality of television pictures”, International Telecommunications Union, Tech. Rep., 2000.

2. Video Quality Experts Group. [Online]. Available: http://www.its.bldrdoc.gov/vqeg/

http://www.its.bldrdoc.gov/vqeg/

1. O. Pele, and M. Werman, “Fast and Robust Earth Mover’s Distances”, in Proc. ICCV, 2009.2. Y. Rubner, et al. “The earth mover’s distance as a metric for image retrieval”. IJCV, vol. 40, no. 2, pp. 99-121, 2000.3. D. Simakov Yaron Caspi et al., “Summarizing visual data using bidirectional similarity”, in Porc. CVPR, 2008.3. C. Barnes, et al., “Patchmatch: a randomized correspondence algorithm for structural image editing”, in Proc. SIGGRAPH,

2009.4. B. S. Manjunath, J. R. Ohm, V. V. Vasudevan, and A. Yamada, “Color and texture descriptors”, IEEE Trans. Circuits Syst.

Video Techno., vol. 11, no. 6, pp. 703-715, Jun. 2001.5. C. Liu, et al., “SIFT flow: dense correspon-dence across different scenes”, in Proc. ECCV, 2008.


• Retargeted image quality metrics– Earth mover’s distance (EMD)1,2

– Bidirectional similarity (BDS)3,4

– Edge histogram4

– SIFT-flow5

44


45

EMD: Represents feature distribution

BDS: information loss Captures how much information one image conveys of the other

SIFT-flow: object shape Detects the correspondence between two images

EH: object shape Edge histogram

Fusion of the four metrics: considering both information loss and object shape

Outline




46

Conclusions

• Overview of the quality assessment development

• Traditional 2D visual signals• Newly emerged visual signals

– Scalable and mobile video– HDR image– Image segmentation– 3D image/video– Retargeted image quality assessment

47

48

Thank you!

overview of quality assessment for visual signals and newly emerged trends 1 king n. ngan †, and...

Documents

perceptual quality

overview of quality

form of digital visual

video quality experts

hong kong slide

signal processing

original signal

peak signal