Skip to main content
SpringerLink
Log in
Book cover

Visual Quality Assessment for Natural and Medical Image pp 63–106Cite as

Image Quality Assessment Based on Human Visual System Properties

  • Chapter
  • First Online:

  • 807 Accesses

Abstract

Utilizing the properties of human visual system (HVS) is a major source of inspirations for the design of image quality assessment (IQA) methods. With the current research status of neuroscience and human vision perception, although a rigorous simulation of HVS is still far from possible, novel ideas can be enlightened. The basic structures of HVS have been previously discussed in Chap. 3, and the goal of this chapter is to connect IQA design with certain knowledge about HVS that can be made use of. More specifically, because we have now been aware that the operation of HVS is actually under a hierarchical structure, it is feasible to study the characteristics of its individual processing stages; on the other hand, if the inner structures are neglected and the HVS is regarded as a Black box, studying its external responses is another potential for providing solutions. This chapter will provide introduction to methods employing these strategies.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Achanta, R., Hemami, S., Estrada, F. & Susstrunk, S. (2009). Frequency-tuned salient region detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (pp. 1597–1604).

    Google Scholar 

  • Ahumada, A. J., & Peterson, H. A. (1992). Luminance-model-based DCT quantization for color image compression. Proc. SPIE on Human Vision, Visual Processing, and Digital Display III, 1666, 365–374.

    Article  Google Scholar 

  • Avraham, T., & Lindenbaum, M. (2010). Esaliency (extended saliency): Meaningful attention using stochastic image modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(4), 693–708.

    Article  Google Scholar 

  • Barten, P. (1999). Contrast sensitivity of the human eye and its effects on image quality. SPIE Press.

    Google Scholar 

  • Baylor, D. A., Lamb, T. D., & Yau, K. W. (1979). Responses of retinal rods to single photons. The Journal of Psychology, 288, 613–634.

    Google Scholar 

  • Bengio, Y. (2009). Learning deep hierarchies for AI. Foundations and Trends in Machine Learning, 2(1), 1–127.

    Article  MATH  Google Scholar 

  • Bex, P. J., & Makous, W. (2002). Spatial frequency, phase, and the contrast of natural images. Journal of the Optical Society of America A, 19(6), 1096–1106.

    Article  Google Scholar 

  • Bian, P., & Zhang, L. (2009). Biological plausibility of spectral domain approach for spatiotemporal visual saliency. Advances in Neuro-Information Processing, 5506, 251–258.

    Article  Google Scholar 

  • Blakemore, C., & Campbell, F. W. (1969). On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images. The Journal of Physiology, 203(1), 237–260.

    Article  Google Scholar 

  • Boring, E. G. (1942). Sensation and perception in the history of experimental psychology. New York: Appleton-Century.

    Google Scholar 

  • Borji, A., & Itti, L. (2013). State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 185–207.

    Article  Google Scholar 

  • Brandao, T., & Queluz, M. P. (2008). No-reference image quality assessment based on DCT domain statistics. Signal Processing, 88(4), 822–833.

    Article  MATH  Google Scholar 

  • Bruce, N. D. B., & Tsotsos, J. K. (2009). Saliency, attention, and visual search: An information theoretic approach. Journal of Vision, 9(3), 1–24.

    Article  Google Scholar 

  • Burt, P. J. (1981). Fast filter transform for image processing. Computer Graphics and Image Processing, 16, 20–51.

    Article  Google Scholar 

  • Burt, P. J., & Adelson, E. H. (1983). The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, 9(4), 532–540.

    Article  Google Scholar 

  • Chandler, D. M., & Hemami, S. S. (2007). VSNR: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Transactions on Image Processing, 16(9), 2284–2298.

    Article  MathSciNet  Google Scholar 

  • Chang, H., Zhang, Q., Wu, Q., & Gan, Y. (2015). Perceptual image quality assessment by independent feature detector. Neurocomputing, 151(3), 1142–1152.

    Article  Google Scholar 

  • Chiu, Y. J., & Berger, T. (1999). A software-only videocodec using pixelwise conditional differential replenishment and perceptual enhancements. IEEE Transactions on Circuits and Systems for Video Technology, 9(3), 438–450.

    Article  Google Scholar 

  • Chou, C. H., & Li, Y. C. (1995). A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile. IEEE Transactions on Circuits on Systems for Video Technology, 5(6), 467–476.

    Article  Google Scholar 

  • Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19(90), 297–301.

    Article  MathSciNet  MATH  Google Scholar 

  • Corporation, S. (1997). Sarnoff JND vision model. Contribution to IEEE G-2.1.6 Compression and Processing Subcommittee.

    Google Scholar 

  • Crowley, J. L. (1981). A representation for visual information (Technique Report CMU-RI-TR-82-07). Pennsylvania: Robotics Institute, Carnegie-Mellon University.

    Google Scholar 

  • Crowley, J. L., & Parker, A. C. (1984). A representation for shape based on peaks and ridges in the difference of low-pass transform. IEEE Transactions on Pattern Recognition and Machine Intelligence, 6(2), 156–170.

    Article  Google Scholar 

  • Crowley, J. L., & Sanderson, A. C. (1987). Multiple resolution representation and probabilistic matching of 2-D gray-scale shape. IEEE Transactions on Pattern Recognition and Machine Intelligence, 9(1), 113–121.

    Article  Google Scholar 

  • D’Angelo, A., Li, Z., & Barni, M. (2010). A full-reference quality metric for geometrically distorted images. IEEE Transactions on Image Processing, 19(4), 867–881.

    Article  MathSciNet  MATH  Google Scholar 

  • Daly, S. (1992). The visible difference predictor: An algorithm for the assessment of image fidelity. Proceedings of SPIE, 1616, 2–15.

    Article  Google Scholar 

  • De Valois, R. L., Albrecht, D. G., & Thorell, L. G. (1982). Spatial frequency selectivity of cells in the macaque visual cortex. Vision Research, 22(5), 545–559.

    Article  Google Scholar 

  • Dickinson, S., Leonardis, A., Schiele, B., & Tarr, M. J. (2009). Objective categorization: Computer and human vision perspectives. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Ding, Y., Wang, S., & Zhang, D. (2014a). Full-reference image quality assessment using statistical local correlation. Electronics Letters, 50(2), 79–80.

    Article  Google Scholar 

  • Ding, Y., Zhang, Y., Wang, X., Yan, X., & Krylov, A. S. (2014b). Perceptual image quality assessment metric using mutual information of Gabor features. Science China: Information Science, 57(3), 032111.

    MATH  Google Scholar 

  • Ding, Y., Zhao, X., Zhang, Z., & Dai, H. (2017a). Image quality assessment based on multi-order local features description, modeling and quantification. IEICE Transactions on Information and Systems, E, 100D(6), 1303–1315.

    Article  Google Scholar 

  • Ding, Y., Zhao, Y., & Zhao, X. (2017b). Image quality assessment based on multi-feature extraction and synthesis with support vector regression. Signal Processing: Image Communication, 54, 81–92.

    Google Scholar 

  • Du, S., Yan, Y., & Ma, Y. (2016). Blind image quality assessment with the histogram sequence of high-order local derivative patterns. Digital Image Processing, 55, 1–12.

    Article  Google Scholar 

  • Engelke, U., Kaprykowsky, H., Zepernick, H. J., & Ndjiki-Nya, P. (2011). Visual attention in quality assessment. IEEE Signal Processing Magazine, 28(6), 50–59.

    Article  Google Scholar 

  • Farias, M. C. Q., & Akamine, W. Y. L. (2012). On performance of image quality metrics enhanced with visual attention computational models. Electronics Letters, 48(11), 631–633.

    Article  Google Scholar 

  • Faugeras, O. D., & Pratt, W. K. (1980). Decorrelation methods of texture feature extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(4), 323–332.

    Article  Google Scholar 

  • Felleman, D., & Essen, D. C. (1991). Distributed hierarchical processing in primate cerebral cortex. Cerebral Cortex, 1, 1–47.

    Article  Google Scholar 

  • Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of Optical Society of America A, 4(12), 2379–2397.

    Article  Google Scholar 

  • Freitas, P. G., Akamine, W. Y. L. & Farias, M. C. Q. (2016). No-reference image quality assessment based on statistics of local ternary pattern. In 8th International Conference on Quality of Multimedia Experience, June 6–8, Lisbon, Portugal.

    Google Scholar 

  • Gagalowicz, A. (1981). A new method for texture fields synthesis: Some applications to the study of human vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(5), 520–533.

    Article  Google Scholar 

  • Gao, X., Gao, F., Tao, D., & Li, X. (2013). Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning. IEEE Transactions on Neural Networks and Learning Systems, 24(12), 2013–2026.

    Article  Google Scholar 

  • Gao, X., Lu, W., Tao, D., & Li, X. (2009). Image quality assessment based on multiscale geometric analysis. IEEE Transactions on Image Processing, 18(7), 1409–1423.

    Article  MathSciNet  MATH  Google Scholar 

  • Garcia-Diaz, A., Fdez-Vidal, X. R., Pardo, X. M., & Dosil, R. (2012). Saliency from hierarchical adaptation through decorrelation and variance normalization. Image and Vision Computing, 30(1), 51–64.

    Article  Google Scholar 

  • Gdyczynski, C. M., Manbachi, A., Hashemi, S., Lashkari, B., & Cobbold, R. S. C. (2014). On estimating the directionality distribution in pedicle trabecular bone from micro-CT images. Physiological Measurement, 35(12), 2415–2428.

    Article  Google Scholar 

  • Girod, B. (1993). What’s wrong with mean-squared error? In Visual factors of electronic image communications. Cambridge: MIT Press.

    Google Scholar 

  • Gu, K., Liu, M., Zhai, G., Yang, X., & Zhang, W. (2015). Quality assessment considering viewing distance and image resolution. IEEE Transactions on Broadcasting, 61(3), 520–531.

    Article  Google Scholar 

  • Guo, C., Ma, Q., & Zhang, L. (2008). Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform. In Proceedings of IEEE Computer Society Conference on Computer Society Conference on Computer Vision and Pattern Recognition, (pp. 1–8).

    Google Scholar 

  • Guo, C., & Zhang, L. (2010). A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Transactions on Image Processing, 19(1), 185–198.

    Article  MathSciNet  MATH  Google Scholar 

  • Hahn, P. J., & Mathews, V. J. (1998). An analytical model of the perceptual threshold function for multichannel image compression. Proceedings of IEEE International Conference on Image Processing, 3, 404–408.

    Google Scholar 

  • Harel, J., Koch, C., & Perona, P. (2007). Graph-based visual saliency. In Advances in Neural Information Processing Systems 19, Proceedings of the 2006 Conference, (pp. 545–552).

    Google Scholar 

  • Hecht, S., Shlar, S., & Pirenne, M. H. (1942). Energy, quanta, and vision. Journal of General Physiology, 25, 819–840.

    Article  Google Scholar 

  • Heeger, D., & Bergen, J. (1995). Pyramid-based texture analysis/synthesis. In Proceeding of ACM SIGGRAPH, (pp. 229–238).

    Google Scholar 

  • Hong, R., Pan, J., Hao, S., Wang, M., Xue, F., & Wu, X. (2014). Image quality assessment based on matching pursuit. Information Sciences, 273, 196–211.

    Article  Google Scholar 

  • Hou, X., & Zhang, L. (2007). Saliency detection: A spectral residual approach. In IEEE Conference on Computer Vision and Pattern Recognition, (pp. 2280–2287).

    Google Scholar 

  • Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology, 160(1), 106–154.

    Article  Google Scholar 

  • Hubel, D. H., & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195(1), 215–243.

    Article  Google Scholar 

  • Hurvich, L. (1981). Color vision. Sunderland: Sinauer Associates Inc.

    Google Scholar 

  • Hyvärinen, A., Hurri, J., & Hoyer, P. O. (2009). Natural image statistics: A probabilistic approach to early computational vision. Berlin: Springer.

    Book  MATH  Google Scholar 

  • Itti, L., & Baldi, P. (2009). Bayesian surprise attracts human attention. Vision Research, 49(10), 1295–1306.

    Article  Google Scholar 

  • Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259.

    Article  Google Scholar 

  • Jia, Y., Lin, W., & Kassim, A. A. (2006). Estimating just-noticeable distortion for video. IEEE Transactions on Circuits and Systems for Video Technology, 16(7), 820–829.

    Article  Google Scholar 

  • Jiang, Q., Shao, F., Jiang, G., Yu, M., & Peng, Z. (2015). Supervised dictionary learning for blind image quality assessment using quality-constraint sparse coding. Journal of Visual Communication and Image Representation, 33, 123–133.

    Article  Google Scholar 

  • Kandel, E. R., Schwartz, J. H., & Jessel, T. M. (2000). Principles of neural sciences. New York: McGraw-Hill.

    Google Scholar 

  • Kingdom, F. A. A., Hayes, A., & Field, D. J. (1995). Sensitivity to contrast histogram differences in synthetic wavelet-textures. Vision Research, 41(5), 585–598.

    Article  Google Scholar 

  • Koch, C., & Ullman, S. (1985). Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4(4), 219–227.

    Google Scholar 

  • Kremers, J. (2005). The primate visual system: A comparative approach. Hoboken: Wiley.

    Book  Google Scholar 

  • Krüger, N., Janssen, P., Kalkan, S., Lappe, M., Leonardis, A., Piater, J., et al. (2013). IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1847–1871.

    Article  Google Scholar 

  • Larson, E. C., & Chandler, D. M. (2008). Unveiling relationships between regions of interest and image fidelity metrics. Proceedings of the Society of Photo-optical Instrumentation Engineers, 6822: 6822A1-16.

    Google Scholar 

  • Larson, E. C., & Chandler, D. M. (2010). Most apparent distortion: Full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19(1), 011006.

    Article  Google Scholar 

  • Le Meur, O., Le Callet, P., Barba, D., & Thoreau, D. (2006). A coherent computational approach to model bottom-up visual attention. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(5), 802–817.

    Article  Google Scholar 

  • Legras, R., Chanteau, N., & Charman, W. N. (2004). Assessment of just-noticeable differences for refractive errors and spherical aberration using visual simulation. Optometry and Vision Science, 81(9), 718–728.

    Article  Google Scholar 

  • Lewis, J., & Essen, D. C. (2000). Corticocortical connections of visual, sensorimotor, and multimodal processing areas in the parietal lobe of the macaque monkey. Journal of Comparative Neurology, 428(1), 112–137.

    Article  Google Scholar 

  • Li, J., Duan, L. Y., Chen, X., Huang, T., & Tian, Y. (2015). Finding the secret of image saliency in the frequency domain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(12), 2428–2440.

    Article  Google Scholar 

  • Li, Q., & Wang, Z. (2009). Reduced-reference image quality assessment using divisive normalization-based image representation. IEEE Journal of Selected Topics in Signal Processing, 3(2), 202–211.

    Article  Google Scholar 

  • Li, S., Zhang, F., Ma, L., & Ngan, K. N. (2011). Image quality assessment by separately evaluating detail losses and additive impairments. IEEE Transactions on Multimedia, 13(5), 935–949.

    Article  Google Scholar 

  • Li, X., Tao, D., Gao, X., & Lu, W. (2009). A natural image quality evaluation metric. Signal Processing, 89(4), 548–555.

    Article  MATH  Google Scholar 

  • Lin, J., Liu, T., Lin, W., & Kuo, C. (2013). Visual-saliency-enhanced image quality assessment indices. In Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, (pp. 1–4).

    Google Scholar 

  • Lin, W., Dong, L., & Xue, P. (2003). Discriminative analysis of pixel difference towards picture quality prediction. Proceedings of IEEE International Conference on Image Processing, 3, 193–196.

    Google Scholar 

  • Liu, H., & Heynderickx, I. (2011). Visual attention in objective image quality assessment: Based on eye-tracking data. IEEE Transactions on Circuits and Systems for Video Technology, 21(7), 971–982.

    Article  Google Scholar 

  • Liu, L., Dong, H., Huang, H., & Bovik, A. C. (2014a). No-reference image quality assessment in curvelet domain. Signal Processing: Image Communication, 29(4), 494–505.

    Google Scholar 

  • Liu, M., & Yang, X. (2009). Image quality assessment using contourlet transform. Optical Engineering, 48(10), 107201.

    Article  Google Scholar 

  • Liu, X., Sun, C., & Yang, L. T. (2015). DCT-based objective quality assessment metric of 2D/3D image. Multimedia Tools and Applications, 74(8), 2803–2820.

    Article  Google Scholar 

  • Liu, Z., Zou, W., & Le Meur, O. (2014b). Saliency tree: A novel saliency detection framework. IEEE Transactions on Image Processing, 23(5), 1937–1952.

    Article  MathSciNet  MATH  Google Scholar 

  • Lu, W., Gao, X., Tao, D., & Li, X. (2008). A wavelet-based image quality assessment method. International Journal of Wavelets Multiresolution and Information, 6(4), 541–551.

    Article  MathSciNet  MATH  Google Scholar 

  • Lu, W., Zeng, K., Tao, D., Yuan, Y., & Gao, X. (2010). No-reference image quality assessment in contourlet domain. Neurocomputing, 73(4–6), 784–794.

    Article  Google Scholar 

  • Lubin, J. (1993). The use of psychophysical data and models in the analysis of display system performance. In A. B. Watson (Ed.), Digital Images and Human Vision (pp. 163–178). Cambridge: MIT Press.

    Google Scholar 

  • Lubin, J. (1995). A visual discrimination mode for image system design and evaluation. In E. Peli (Ed.), Visual models for target detection and recognition (pp. 207–220). Singapore: World Scientific Publishers.

    Google Scholar 

  • Ma, L., Li, S., & Ngan, K. N. (2013). Reduced-reference image quality assessment in reorganized DCT domain. Signal Processing: Image Communication, 28(8), 884–902.

    Google Scholar 

  • Ma, L., Li, S., Zhang, F., & Ngan, K. N. (2011). Reduced-reference image quality assessment using reorganized DCT-based image representation. IEEE Transactions on Multimedia, 13(4), 824–829.

    Article  Google Scholar 

  • Ma, L., Wang, X., Liu, Q., & Ngan, K. N. (2016). Reorganized DCT-based image representation for reduced reference stereoscopic image quality assessment. Neurocomputing, 215(SI), 21–31.

    Article  Google Scholar 

  • Ma, Q., & Zhang, L. (2008). Saliency-based image quality assessment criterion. Advanced Intelligent Computing Theories and Applications, International Conference on Intelligent Computing, 5226, 1124–1133.

    Google Scholar 

  • Ma, Q., Zhang, L., & Wang, B. (2010). New strategy for image and video quality assessment. Journal of Electronic Imaging, 19(1), 1–14.

    Article  Google Scholar 

  • Manduchi, R., Perona, P., & Shy, D. (1997). Efficient deformable filter banks. IEEE Transactions on Signal Processing, 46(4), 1168–1173.

    Article  Google Scholar 

  • Mannos, J. L., & Sakrison, D. J. (1974). The effects of a visual fidelity criterion on the images. IEEE Transactions on Information Theory, 20(4), 525–536.

    Article  MATH  Google Scholar 

  • Marat, S., Phuoc, T. H., Granjon, L., Guyader, N., Pellerin, D., & Guerin-Dugue, A. (2009). Modeling spatio-temporal saliency to predict gaze direction for short videos. International Journal of Computer Vision, 82(3), 231–243.

    Article  Google Scholar 

  • Marr, D. (1977). Vision: A computational investigation into the human representation and processing of visual information. Freeman.

    Google Scholar 

  • Maunsell, J. H. R., & Essen, D. C. (1983). Functional properties of neurons in middle temporal area of the macaque monkey, I, selectivity for stimulus direction, speed, and orientation. Journal of Neurophysiology, 49(5), 1127–1147.

    Article  Google Scholar 

  • McCann, J. J., McKee, S. P., & Taylor, T. H. (1976). Quantitative studies in retinex theory a comparison between theoretical predictions and observer responses to the “color mondrian” experiments. Vision Research, 16(5), 445–458.

    Article  Google Scholar 

  • Meer, P., Baugher, E. S., & Rosenfeld, A. (1987). Frequency domain analysis and synthesis of image generating kernels. IEEE Transactions on Pattern Recognition and Machine Intelligence, 9(4), 512–522.

    Article  Google Scholar 

  • Moorthy, A. K., & Bovik, A. C. (2009). Visual importance pooling for image quality assessment. IEEE Journal of Selected Topics in Signal Processing, 3(2), 193–201.

    Article  Google Scholar 

  • Moulden, B., Kingdom, F. A. A., & Gatley, L. F. (1990). The standard deviation of luminance as a metric for contrast in random-dot images. Perception, 19(1), 79–101.

    Article  Google Scholar 

  • Nakamura, H., Kuroda, T., Wakita, M., Kusunoki, M., Kato, A., Mikami, A., et al. (2001). From three-dimensional space vision to prehensile hand movements: The lateral intraparietal area links the area V3A and the anterior intraparietal area in macaques. Journal of Neuroscience, 21(20), 8174–8187.

    Google Scholar 

  • Narwaria, M., Lin, W., McLoughlin, I. V., Emmanuel, S., & Chia, L. T. (2012). Fourier transform-based scalable image quality measure. IEEE Transactions on Image Processing, 21(8), 3364–3377.

    Article  MathSciNet  MATH  Google Scholar 

  • Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29, 51–59.

    Article  Google Scholar 

  • Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.

    Article  MATH  Google Scholar 

  • Orban, G. A. (2008). Higher order visual processing in macaque extrastriate cortex. Physiological Reviews, 88, 59–89.

    Article  Google Scholar 

  • Pati, P. B., & Ramakrishnan, A. G. (2008). Word level multi-script identification. Pattern Recognition Letters, 29, 1218–1229.

    Article  Google Scholar 

  • Peters, R., Iyer, A., Itti, L., & Koch, C. (2005). Components of bottom-up gaze allocation in natural images. International Journal of Neural Systems, 45(18), 2397–2416.

    Google Scholar 

  • Portilla, J., & Simoncelli, E. P. (2000). A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision, 40, 49–71.

    Article  MATH  Google Scholar 

  • Poynton, C. (1998). The rehabilitation of gamma. In Proceedings of SPIE Human Vision and Electronic Imaging, pp. 232–249.

    Google Scholar 

  • Prewitt, J. M. S. (1970). Object enhancement and extraction. In B. S. Lipkin & A. Rosenfeld (Eds.), Picture processing and psychopictorics. Cambridge: Academic Press.

    Google Scholar 

  • Qian, J., Wu, D., Li, L., Cheng, D., & Wang, X. (2014). Image quality assessment based on multi-scale representation of structure. Digital Signal Processing, 33, 125–133.

    Article  Google Scholar 

  • Raju, S. S., Pati, P. B., & Ramakrishnan, A. G. (2004). Gabor filter based block energy analysis for text extraction from digital document images. In Proceedings of the 1st International Workshop on Document Image Analysis for Libraries, (pp. 233–243).

    Google Scholar 

  • Ramasubramanian, M., Pattanaik, S. N., & Greenberg, D. P. (1999). A perceptually based physical error metric for realistic image synthesis. In Proceedings of International Conference on Computer Graphics and Interactive Techniques, (pp. 73–82).

    Google Scholar 

  • Ramos, M. G., & Hemami, S. S. (2001). Suprathreshold wavelet coefficient quantization in complex stimuli: Psychophysical evaluation and analysis. Journal of the Optical Society of America A, 18(10), 2385–2397.

    Article  Google Scholar 

  • Rezazadeh, S., & Coulombe, S. (2013). A novel discrete wavelet transform framework for full reference image quality assessment. Signal, Image and Video Processing, 7(3), 559–573.

    Article  Google Scholar 

  • Roberts, L. G. (1965). Machine perception of three-dimensional solids. In J. T. Tippet (Ed.), Optical and electro-optical information processing. Cambridge: MIT Press.

    Google Scholar 

  • Rodríguez-Sánchez, A. J., Simine, E., & Tsotsos, J. (2007). Attention and visual search. International Journal of Neural Systems, 17(4), 275–288.

    Article  Google Scholar 

  • Saad, M. A., Bovik, A. C., & Charrier, C. (2010). A DCT statistics-based blind image quality index. IEEE Signal Processing Letters, 17(6), 583–586.

    Article  Google Scholar 

  • Saad, M. A., Bovik, A. C., & Charrier, C. (2012). Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing, 21(8), 3339–3352.

    Article  MathSciNet  MATH  Google Scholar 

  • Saha, A., & Wu, Q. M. J. (2013). Perceptual image quality assessment using phase deviation sensitive energy features. Signal Processing, 93(11), 3182–3191.

    Article  Google Scholar 

  • Safranek, R. J., & Johnston, J. D. (1989). A perceptually tuned sub-band image coder with image dependence quantization and post-quantization data compression. In Proceedings of IEEE Conference on Acoustic, Speech, and Signal Processing, (pp. 1945–1948).

    Google Scholar 

  • Sezan, M. I., Yip, K. L., & Daly, S. (1987). Uniform perceptual quantization: Applications to digital radiography. IEEE Transactions on Systems, Man, and Cybernetics, 17(4), 622–634.

    Article  Google Scholar 

  • Sheikh, H. R., & Bovik, A. C. (2006). Image information and visual quality. IEEE Transactions on Image Processing, 15(2), 430–444.

    Article  Google Scholar 

  • Sheikh, H. R., Bovik, A. C., & Cormack, L. (2005a). No-reference quality assessment using natural scene statistics: JPEG2000. IEEE Transactions on Image Processing, 14(11), 1918–1927.

    Article  Google Scholar 

  • Sheikh, H. R., Bovik, A. C., & de Vaciana, G. (2005b). An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Transactions on Image Processing, 14(12), 2117–2128.

    Article  Google Scholar 

  • Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006). A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 15(11), 3440–3451.

    Article  Google Scholar 

  • Shen, J., Li, Q., & Erlebacher, G. (2011). Hybrid no-reference natural image quality assessment of noisy, blurry, JPEG2000, and JPEG images. IEEE Transactions on Image Processing, 20(8), 2089–2098.

    Article  MathSciNet  MATH  Google Scholar 

  • Sobel, I. E. (1970). Camera models and machine perception. Ph.D. Dissertation. California: Stanford University.

    Google Scholar 

  • Soundararajan, R., & Bovik, A. C. (2012). RRED indices: Reduced reference entropic differencing for image quality assessment. IEEE Transactions on Image Processing, 21(2), 517–526.

    Article  MathSciNet  MATH  Google Scholar 

  • Stromeyer, C. F., & Julesz, B. (1972). Spatial-frequency masking in vision: Critical bands and spread of masking. Journal of the Optical Society of America, 62(10), 1221–1232.

    Article  Google Scholar 

  • Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19(6), 1635–1650.

    Article  MathSciNet  MATH  Google Scholar 

  • Tanaka, K. (1996). Inferotemporal cortex and object vision. Annual Review of Neuroscience, 19(1), 109–139.

    Article  MathSciNet  Google Scholar 

  • Tanaka, K., & Saito, H. A. (1989). Analysis of motion of the visual field by direction, expansion/contraction, and rotation cells clustered in the dorsal part of the medial superior temporal area of the macaque monkey. Journal of Neurophysiology, 62(3), 626–641.

    Article  Google Scholar 

  • Tenenbaum, F. E., David, S. V., Singh, N. C., Hsu, A., Vinje, W. E., & Gallant, J. L. (2001). Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network: Computation in Neural Systems, 12(3), 289–316.

    Article  MATH  Google Scholar 

  • Teo, P. C., & Heeger, D. J. (1994). Perceptual image distortion. Proceedings of SPIE, 2179, 127–141.

    Article  Google Scholar 

  • Tiippana, K., Näsänen, R., & Rovamo, J. (1994). Contrast matching of two-dimensional compound gratings. Vision Research, 34(9), 1157–1163.

    Article  Google Scholar 

  • Toet, A. (2011). Computational versus psychophysical bottom-up image saliency: A comparative evaluation study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11), 2131–2146.

    Article  Google Scholar 

  • Tong, Y., Konik, H., Cheikh, F. A., & Tremeau, A. (2010). Full reference image quality assessment based on saliency map analysis. Journal of Imaging Science and Technology, 54(3), 30503:1–30503:14.

    Article  Google Scholar 

  • Toprak, S., & Yalman, Y. (2017). A new full-reference image quality metric based on just noticeable difference. Computer Standards & Interfaces, 50, 18–25.

    Article  Google Scholar 

  • Torralba, A. (2003). Modeling global scene factors in attention. Journal of the Optical Society of America A-Optics Image Science and Vision, 20(7), 1407–1418.

    Article  Google Scholar 

  • Ungerleider, L. G., & Mishkin, M. (1982). Two cortical visual systems. In D. J. Ingle, M. A. Goodale, & R. J. W. Mansfield (Eds.), Analysis of visual behavior (pp. 549–586). Cambridge: MIT Press.

    Google Scholar 

  • Uzair, M., & Dony, R. D. (2017). Estimating just-noticeable distortion for images/videos in pixel domain. IET Image Processing, 11(8), 559–567.

    Article  Google Scholar 

  • Van Nes, F. L., & Bouman, M. A. (1967). Spatial modulation transfer in the human eye. Journal of the Optical Society of America, 57(3), 401–406.

    Article  Google Scholar 

  • Walther, D., & Koch, C. (2006). Modeling attention to salient proto-objects. Neural Networks, 19(9), 1395–1407.

    Article  MATH  Google Scholar 

  • Wang, C., Shen, M., & Yao, C. (2015). No-reference quality assessment for DCT-based compressed image. Journal of Visual Communication and Image Representation, 28, 53–59.

    Article  Google Scholar 

  • Wang, S., Gu, K., Ma, S., Lin, W., Liu, X., & Gao, W. (2016). Guided image contrast enhancement based on retrieved images in cloud. IEEE Transactions on Multimedia, 18(2), 219–232.

    Article  Google Scholar 

  • Wang, Z., & Bovik, A. C. (2006). Modern image quality assessment. New York: Morgan & Claypool.

    Google Scholar 

  • Wang, Z., & Bovik, A. C. (2009). Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE Signal Processing Magazine, 26(1), 98–117.

    Article  Google Scholar 

  • Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

  • Watson, A. B. (1987a). Estimation of local spatial scale. Journal of the Optical Society of America A, 5(4), 2401–2417.

    Article  Google Scholar 

  • Watson, A. B. (1987b). The Cortex transform: Rapid computation of simulated neural images. Computer Vision Graphics and Image Processing, 39(3), 311–327.

    Article  Google Scholar 

  • Watson, A. B., & Ahumanda, A. (2005). A standard model for foveal detection of spatial contrast. Journal of Vision, 5(9), 717–740.

    Article  Google Scholar 

  • Watson, A. B., Yang, G. Y., Solomon, J. A., & Villasenor, J. (1997). Visibility of wavelet quantization noise. IEEE Transactions on Image Processing, 6(8), 1164–1175.

    Article  Google Scholar 

  • Wei, Z., & Ngan, K. N. (2009). Spatio-temporal just noticeable distortion profile for grey scale image/video in DCT domain. IEEE Transactions on Circuits and Systems for Video Technology, 19(3), 337–346.

    Article  Google Scholar 

  • Wen, Y., Li, Y., Zhang, X., Shi, W., Wang, L., & Chen, J. (2017). A weighted full-reference image quality assessment based on visual saliency. Journal of Visual Communication and Image Representation, 43, 119–126.

    Article  Google Scholar 

  • Willmore, B. D. B., Prenger, R. J., & Gallant, J. L. (2010). Neural representation of natural images in visual area V2. Journal of Neuroscience, 30(6), 2102–2114.

    Article  Google Scholar 

  • Wilson, H., & Bergen, J. (1979). A four-mechanism model for threshold spatial vision. Vision Research, 19(1), 19–32.

    Article  Google Scholar 

  • Wu, H. R., & Rao, K. R. (2005). Digital image video quality and perceptual coding. Florida: CRC Press.

    Book  Google Scholar 

  • Wu, Q., Li, H., Meng, F., Ngan, K. N., Luo, B., Huang, C., et al. (2016). Blind image quality assessment based on multichannel feature fusion and label transfer. IEEE Transactions on Circuits and Systems for Video Technology, 26(3), 425–440.

    Article  Google Scholar 

  • Xue, W., Mou, X., Zhang, L., Bovik, A. C., & Feng, X. (2014). Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features. IEEE Transactions on Image Processing, 23(11), 4850–4862.

    Article  MathSciNet  MATH  Google Scholar 

  • Yang, X., Lin, W., Lu, Z., Ong, E. P., & Yao, S. (2003a). Just-noticeable-distortion profile with nonlinear additivity model for perceptual masking in color images. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 3, 609–612.

    Google Scholar 

  • Yang, X., Lin, W., Lu, Z., Ong, E. P., & Yao, S. (2003b). Perceptually-adaptive hybrid video encoding based on just-noticeable-distortion profile. Proceedings of the Society of Photo-optical Instrumentation Engineers, 5150, 1448–1459.

    Google Scholar 

  • Ye, P., & Doermann, D. (2012). No-reference image quality assessment using visual codebooks. IEEE Transactions on Image Processing, 21(7), 3129–3138.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhai, G., Zhang, W., Yang, X., Lin, W., & Xu, Y. (2008). No-reference noticeable blockiness estimation in images. Signal Processing: Image Communication, 23(6), 417–432.

    Google Scholar 

  • Zhang, L., & Li, H. (2012). SR-SIM: A fast and high performance IQA index based on spectral residual. In Proceedings of IEEE International Conference on Image Processing, (pp. 1473–1476).

    Google Scholar 

  • Zhang, L., Gu, Z., & Li, H. (2013a). SDSP: A novel saliency detection method by combining simple priors. In Proceedings of IEEE International Conference on Image Processing, (pp. 171–175).

    Google Scholar 

  • Zhang, L., Shen, Y., & Li, H. (2014a). VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10), 4270–4281.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang, L., Tong, M. H., Marks, T. M., Shan, H., & Cottrell, G. W. (2008a). SUN: A Bayesian framework for saliency using natural statistics. Journal of Vision, 8(7), 32.

    Article  Google Scholar 

  • Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20(8), 2378–2386.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang, M., Mou, X., Fujita, H., Zhang, L., Zhang, X., & Xue, W. (2013b). Local binary pattern statistics feature for reduced reference image quality assessment. Proceedings of SPIE, 8660(86600L), 1–8.

    Google Scholar 

  • Zhang, W., Borji, A., Wang, Z., Le Callet, P., & Liu, H. (2016). The application of visual saliency models in objective image quality assessment: A statistical evaluation. IEEE Transactions on Neural Networks and Learning Systems, 27(6), 1266–1278.

    Article  MathSciNet  Google Scholar 

  • Zhang, X., Lin, W., & Xue, P. (2008b). Just-noticeable difference estimation with pixels in images. Journal of Visual Communication and Image Representation, 19(1), 30–41.

    Article  Google Scholar 

  • Zhang, Y., & Chandler, D. M. (2013). No-reference image quality assessment based on log-derivative statistics of natural scenes. Journal of Electronic Imaging, 22(4), 043025.

    Article  Google Scholar 

  • Zhang, Y., Moorthy, A. K., Chandler, D. M., & Bovik, A. C. (2014b). D-DIIVINE: No-reference image quality assessment based on local magnitude and phase statistics of natural scenes. Signal Processing: Image Communication, 29(7), 725–747.

    Google Scholar 

  • Zhao, Y., Ding, Y., & Zhao, X. (2016). Image quality assessment based on complementary local feature extraction and quantification. Electronics Letters, 52(22), 1849–1850.

    Article  Google Scholar 

  • Zhu, W., Liang, S., Wei, Y., & Sun, J. (2014). Saliency optimization from robust background detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (pp. 2814–2821).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Yong Ding

Authors
  1. Yong Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Ding .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Zhejiang University Press, Hangzhou and Springer-Verlag GmbH Germany

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ding, Y. (2018). Image Quality Assessment Based on Human Visual System Properties. In: Visual Quality Assessment for Natural and Medical Image. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56497-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-56497-4_5

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-56495-0

  • Online ISBN: 978-3-662-56497-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation