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Image and Video Quality Assessment

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Advanced Video Coding Systems
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Abstract

Recently, there is a striking rise in the interest and demand of accurate image quality assessment algorithms. Image and video quality assessment plays an important role in optimizing, benchmarking, monitoring, and inspecting multimedia systems. Since the human visual system (HVS) is the ultimate receiver of the visual signals, developing quality assessment algorithms that align well with human visual perception is crucial in system design and optimization. The straightforward way to evaluate the quality is subjective viewing by many observers with standard procedures. However, it is time-consuming, expensive, and also difficult for online applications. In this chapter, we will review the recent advances of objective quality assessment, including the image, video, and 3D-video quality assessment.

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References

  • Barland R, Saadane A (2005) Reference free quality metric for JPEG-2000 compressed images. In: Proceedings of the eighth international symposium on signal processing and its applications, vol 1. IEEE, pp 351–354

    Google Scholar 

  • Benoit A, Le Callet P, Campisi P, Cousseau R et al (2008) Using disparity for quality assessment of stereoscopic images. In: Proceedings of the 15th IEEE international conference on image processing

    Google Scholar 

  • Brandão T, Queluz MP (2008) No-reference image quality assessment based on DCT domain statistics. Signal Process 88(4):822–833

    Article  MATH  Google Scholar 

  • Brandão T, Queluz MP (2010) No-reference quality assessment of H.264/AVC encoded video. IEEE Trans Circuits Syst Video Technol 20(11):1437–1447

    Article  Google Scholar 

  • Campisi P, Le Callet P, Marini E (2007) Stereoscopic images quality assessment. In: Proceedings of 15th European signal processing conference (EUSIPCO07)

    Google Scholar 

  • Chandler DM, Hemami SS (2007) VSNR: a wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans Image Process 16(9):2284–2298

    Article  MathSciNet  Google Scholar 

  • Chen MJ, Cormack LK, Bovik AC (2013) No-reference quality assessment of natural stereopairs. IEEE Trans Image Process 22(9):3379–3391

    Article  MathSciNet  Google Scholar 

  • De Simone F, Naccari M, Tagliasacchi M, Dufaux F, Tubaro S, Ebrahimi T (2009) Subjective assessment of H. 264/AVC video sequences transmitted over a noisy channel. In: International workshop on quality of multimedia experience, QoMEx 2009. IEEE, pp 204–209

    Google Scholar 

  • Friston K (2010) The free-energy principle: a unified brain theory? Nat Rev Neurosci 11(2):127–138

    Article  Google Scholar 

  • Friston K, Kilner J, Harrison L (2006) A free energy principle for the brain. J Physiol-Paris 100(1):70–87

    Article  Google Scholar 

  • Girod B (1991) Psychovisual aspects of image processing: what’s wrong with mean squared error? In: Proceedings of the seventh workshop on multidimensional signal processing. IEEE, pp P–2

    Google Scholar 

  • Goldmann L, De Simone F, Ebrahimi T (2010) Impact of acquisition distortions on the quality of stereoscopic images. In: 5th international workshop on video processing and quality metrics for consumer electronics (VPQM), Citeseer

    Google Scholar 

  • Gorley P, Holliman N (2008) Stereoscopic image quality metrics and compression. In: Electronic imaging 2008, international society for optics and photonics, 680305-680305-12

    Google Scholar 

  • Gu K, Zhai G, Yang X, Zhang W (2013) A new reduced-reference image quality assessment using structural degradation model. In: IEEE international symposium on circuits and systems (ISCAS). IEEE, pp 1095–1098

    Google Scholar 

  • Huynh-Thu Q, Ghanbari M (2009) No-reference temporal quality metric for video impaired by frame freezing artefacts. In: 16th IEEE international conference on image processing (ICIP). IEEE, pp 2221–2224

    Google Scholar 

  • ITU-T (2004) Objective perceptual video quality measurement techniques for digital cable television in the presence of a full reference. ITU-T Rec J144, Recommendations of the ITU, Telecommunication Standardization Sector

    Google Scholar 

  • Jayaraman D, Mittal A, Moorthy AK, Bovik AC (2012) Objective quality assessment of multiply distorted images. In: Conference record of the forty sixth Asilomar conference on signals, systems and computers (ASILOMAR). IEEE, pp 1693–1697

    Google Scholar 

  • Larson EC, Chandler D (2010) Categorical image quality (CSIQ) database. http://vision.okstate.edu/csiq

  • Li X (2002) Blind image quality assessment. In: Proceedings 2002 international conference on image processing, vol 1. IEEE, pp I–449

    Google Scholar 

  • Liu M, Zhai G, Tan S, Zhang Z, Gu K, Yang X (2014) HDR2014-a high dynamic range image quality database. In: IEEE international conference on multimedia and expo workshops (ICMEW). IEEE, pp 1–6

    Google Scholar 

  • Ma L, Li S, Zhang F, Ngan KN (2011) Reduced-reference image quality assessment using reorganized DCT-based image representation. IEEE Trans Multimed 13(4):824–829

    Article  Google Scholar 

  • Ma L, Lin W, Deng C, Ngan KN (2012) Image retargeting quality assessment: a study of subjective scores and objective metrics. IEEE J Sel Top Signal Process 6(6):626–639

    Article  Google Scholar 

  • Marziliano P, Dufaux F, Winkler S, Ebrahimi T (2004) Perceptual blur and ringing metrics: application to JPEG2000. Signal Process Image Commun 19(2):163–172

    Article  Google Scholar 

  • Mittal A, Moorthy AK , Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708

    Google Scholar 

  • Moorthy AK, Bovik AC (2010) Efficient video quality assessment along temporal trajectories. IEEE Trans Circuits Syst Video Technol 20(11):1653–1658

    Article  Google Scholar 

  • Moorthy AK, Bovik AC (2011) Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans Image Process 20(12):3350–3364

    Google Scholar 

  • Moorthy AK, Su CC, Mittal A, Bovik AC (2013) Subjective evaluation of stereoscopic image quality. Signal Process: Image Commun 28(8):870–883

    Google Scholar 

  • Ninassi A, Le Meur O, Le Callet P, Barba D (2009) Considering temporal variations of spatial visual distortions in video quality assessment. IEEE J Sel Top Signal Process 3(2):253–265

    Article  Google Scholar 

  • Ong E, Lin W, Lu Z, Yang X, Yao S, Pan F, Jiang L, Moschetti F (2003) A no-reference quality metric for measuring image blur. In: Proceedings seventh international symposium on signal processing and its applications, vol 1. IEEE, pp 469–472

    Google Scholar 

  • Ou YF, Ma Z, Liu T, Wang Y (2011) Perceptual quality assessment of video considering both frame rate and quantization artifacts. IEEE Trans Circuits Syst Video Technol 21(3):286–298

    Article  Google Scholar 

  • Pesquet-Popescu B, Véhel JL (2002) Stochastic fractal models for image processing. IEEE Signal Process Mag 19(5):48–62

    Article  Google Scholar 

  • Pinson MH, Wolf S (2004) A new standardized method for objectively measuring video quality. IEEE Trans Broadcast 50(3):312–322

    Article  Google Scholar 

  • Ponomarenko N, Lukin V, Zelensky A, Egiazarian K, Carli M, Battisti F (2009) TID2008-a database for evaluation of full-reference visual quality assessment metrics. Adv Mod Radioelectron 10(4):30–45

    Google Scholar 

  • Ponomarenko N, Ieremeiev O, Lukin V, Egiazarian K, Jin L, Astola J, Vozel B, Chehdi K, Carli M, Battisti F et al (2013) Color image database TID2013: peculiarities and preliminary results. In: 4th European workshop on visual information processing (EUVIP). IEEE, pp 106–111

    Google Scholar 

  • Rehman A, Wang Z (2012) Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans Image Process 21(8):3378–3389

    Article  MathSciNet  Google Scholar 

  • Saad MA, Bovik AC, Charrier C (2012) Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans Image Process 21(8):3339–3352

    Google Scholar 

  • Sazzad ZP, Yamanaka S, Kawayokeita Y, Horita Y (2009) Stereoscopic image quality prediction. In: International workshop on quality of multimedia experience, QoMEx 2009. IEEE, pp 180–185

    Google Scholar 

  • Seshadrinathan K, Soundararajan R, Bovik AC, Cormack LK (2010) Study of subjective and objective quality assessment of video. IEEE Trans Image Process 19(6):1427–1441

    Article  MathSciNet  Google Scholar 

  • Shaked D, Tastl I (2005) Sharpness measure: towards automatic image enhancement. In: IEEE international conference on image processing, ICIP 2005, vol 1. IEEE, pp I–937

    Google Scholar 

  • Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444

    Article  Google Scholar 

  • Sheikh HR, Wang Z, Cormack L, Bovik AC (2005) Live image quality assessment database release 2

    Google Scholar 

  • Soundararajan R, Bovik AC (2012) RRED indices: reduced reference entropic differencing for image quality assessment. IEEE Trans Image Process 21(2):517–526

    Article  MathSciNet  Google Scholar 

  • Wang Z, Bovik AC (2011) Reduced-and no-reference image quality assessment. IEEE Signal Process Mag 28(6):29–40

    Article  Google Scholar 

  • Wang Z, Li Q (2007) Video quality assessment using a statistical model of human visual speed perception. JOSA A 24(12):B61–B69

    Article  Google Scholar 

  • Wang Z, Simoncelli EP (2005) Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In: Electronic imaging 2005, international society for optics and photonics, pp 149–159

    Google Scholar 

  • Wang Z, Simoncelli E, Bovik A (2003) Multiscale structural similarity for image quality assessment. In: Conference record of the thirty-seventh Asilomar conference on signals, systems and computers, vol 2. pp 1398–1402

    Google Scholar 

  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004a) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  • Wang Z, Lu L, Bovik AC (2004b) Video quality assessment based on structural distortion measurement. Signal Process: Image Commun 19(2):121–132

    Google Scholar 

  • Wang Z, Wu G, Sheikh HR, Simoncelli EP, Yang EH, Bovik AC (2006) Quality-aware images. IEEE Trans Image Process 15(6):1680–1689

    Article  Google Scholar 

  • Wang X, Yu M, Yang Y, Jiang G (2009) Research on subjective stereoscopic image quality assessment. In: IS&T/SPIE electronic imaging, international society for optics and photonics, pp 725,509–725,509

    Google Scholar 

  • Wang Y, Jiang T, Ma S, Gao W (2012) Novel spatio-temporal structural information based video quality metric. IEEE Trans Circuits Syst Video Technol 22(7):989–998

    Article  Google Scholar 

  • Wang S, Zhang X, Ma S, Gao W (2013) Reduced reference image quality assessment using entropy of primitives. In: Picture coding symposium (PCS), pp 193–196

    Google Scholar 

  • Wu J, Lin W, Shi G, Liu A (2013) Reduced-reference image quality assessment with visual information fidelity

    Google Scholar 

  • Xue W, Zhang L, Mou X (2013) Learning without human scores for blind image quality assessment. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 995–1002

    Google Scholar 

  • Xue W, Zhang L, Mou X, Bovik A (2014) Gradient magnitude similarity deviation: a highly efficient perceptual image quality index

    Google Scholar 

  • Yang J, Hou C, Zhou Y, Zhang Z, Guo J (2009) Objective quality assessment method of stereo images. In: 3DTV conference: the true vision-capture, transmission and display of 3D video. IEEE, pp 1–4

    Google Scholar 

  • Yang J, Hou C, Xu R, Lei J (2010) New metric for stereo image quality assessment based on HVS. Int J Imaging Syst Technol 20(4):301–307

    Article  Google Scholar 

  • Zhai G, Wu X, Yang X, Lin W, Zhang W (2012) A psychovisual quality metric in free-energy principle. IEEE Trans Image Process 21(1):41–52

    Article  MathSciNet  Google Scholar 

  • Zhang L, Zhang D, Mou X (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    Article  MathSciNet  Google Scholar 

  • Zhou J, Jiang G, Mao X, Yu M, Shao F, Peng Z, Zhang Y (2011) Subjective quality analyses of stereoscopic images in 3DTV system. In: IEEE visual communications and image processing (VCIP). IEEE, pp 1–4

    Google Scholar 

  • Zhu Z, Wang Y (2009) Perceptual distortion metric for stereo video quality evaluation. WSEAS Trans Signal Process 5(7):241–250

    Google Scholar 

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Gao, W., Ma, S. (2014). Image and Video Quality Assessment. In: Advanced Video Coding Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-14243-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-14243-2_11

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