1. Kelly, D.H. (1979) Motion and vision II: Stabilized spatio-temporal threshold surface. Journal of the Optical Society of America, 69 (10), 1340–1349.
2. Legge, G.E. and Foley, J.M. (1980) Contrast masking in human vision. Journal of the Optical Society of America, 70, 1458–1471.
3. Watson, A.B. and Solomon, J.A. (1997) Model of visual contrast gain control and pattern masking. Journal of the Optical Society of America A (Optics, Image Science and Vision), 14 (9), 2379–2391.
4. Pastrana-Vidal, R., Gicquel, J.C., Colomes, C. and Cherifi, H. (2004) Temporal masking effect on dropped frames at video scene cuts. Proceedings of The International Society for Optical Engineering, 5292, 194–201.
5. Daly, S. (2001) Engineering observations from spatiovelocity and spatiotemporal visual models, in Vision Models and Applications to Image and Video Processing (ed. C.J. van den Branden Lambrecht), Kluwer Academic Publishers, Norwell, MA.
6. Jayant, N.S., Johnston, J. and Safranek, R. (1993) Signal compression based on models of human perception. Proceedings of The IEEE, 81, 1385–1422.
7. Yang, X., Lin, W., Lu, Z. et al.(2005) Motion-compensated residue preprocessing in video coding based on Just-Noticeable-Distortion profile. IEEE Transactions on Circuits and Systems for Video Technology, 15, 742–752.
8. Lin, W., Dong, L. and Xue, P. (2005) Visual distortion gauge based on discrimination of noticeable contrast changes. IEEE Transactions on Circuits System Video Technology, 15 (7), 900–909.
9. Lin, W. (2006) Computational models for just-noticeable difference, Chapter 9, in Digital Video Image Quality and Perceptual Coding (eds H.R. Wu and K.R. Rao), CRC Press.
10. Zhang, X., Lin, W. and Xue, P. (2005) Improved estimation for just noticeable visual distortion. Signal Processing, 85 (4), 795–808.
11. Chou, C. and Li, Y. (1995) A perceptually tuned sub-band image coder based on the measure of Just-Noticeable-Distortion profile. IEEE Transactions on Circuits System Video Technology, 5 (6), 467–476.
12. Liu, A., Lin, W., Paul, M. et al.(2010) Just Noticeable Difference for images with decomposition model for separating edge and textured regions. IEEE Transactions on Circuits and Systems for Video Technology, 20 (11), 1648–1652.
13. Chou, C. and Chen, C. (1996) A perceptually optimized 3-D sub-band image codec for video communication over wireless channels. IEEE Transactions on Circuits System Video Technology, 6 (2), 143–156.
14. Yang, X., Lin, W., Lu, Z. et al.(2005) Just noticeable distortion model and its applications in video coding. Signal Processing: Image Communications, 20, 662–680.
15. Lu, Z., Lin, W., Yang, X. et al.(2005) Modeling visual attention's modulatory aftereffects on visual sensitivity and quality evaluation. IEEE Transactions on Image Processing, 14 (11), 1928–1942.
16. Chen, Z. and Guillemot, C. (2010) Perceptually friendly H.264/AVC video coding based on foveated Just-Noticeable-Distortion model. IEEE Transactions on Circuits System Video Technology, 20 (6), 806–819.
17. Wang, Z. and Bovik, A.C. (2001) Embedded foveation image coding. IEEE Transactions on Image Processing, 10 (10), 1397–1410.
18. Ma, Q., Zhang, L. and Wang, B. (2010) New strategy for image and video quality assessment. Journal of Electronic Imaging, 19 (1), 011019, 1–14.
19. Wang, Z., Bovik, A., Sheikh, H. and Simoncelli, E. (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13 (4), 600–612.
20. Channappayya, S., Bovik, A. and Heath, R. (2008) Rate bounds on SSIM Index of quantized images. IEEE Transactions on Image Processing, 17 (9), 1624–1639.
21. Huang, Y., Ou, T., Su, P. and Chen, H. (2010) Perceptual rate-distortion optimization using structural similarity index as quality metric. IEEE Transaction on Circuits System Video Technology, 20 (11), 1614–1624.
22. ITU-R, Recommendation BT.500–11(2002) Methodology for the Subjective Assessment of the Quality of Television Pictures.
23. Wang, Z., Bovik, A. and Lu, L. (2002) Why is image quality assessment so difficult? IEEE International Conference on Acoustics, Speech, & Signal Processing, 4, 3313–3316.
24. Wu, H. and Rao, K. (2005) Digital Video Image Quality and Perceptual Coding, CRC Press.
25. Lin, W. and Kuo, C. (2011) Perceptual visual quality metrics: a survey. Journal of Visual Communication and Image Representation, 22 (4), 297–312.
26. Ponomarenko, N., Silvestri, F., Egiazarian, K. et al.(2007) On between-coefficient contrast masking of DCT basis functions. Proceedings of The Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics.
27. Sheikh, H. and Bovik, A. (2006) Image information and visual quality. IEEE Transactions on Image Processing, 15 (2), 430–444.
28. Chandler, D. and Hemami, S. (2007) VSNR: A wavelet-based visual signal-to-noise-ratio for natural images. IEEE Transactions on Image Processing, 16 (9), 2284–2298.
29. Larson, E. and Chandler, D. (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19 (1), 011006, 1–21.
30. Liu, A., Lin, W. and Narwaria, M. (2012) Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing, 21 (4), 1500–1512.
31. Wang, Z., Simoncelli, E. and Bovik, A. (2003) Multiscale structural similarity for image quality assessment. Proceedings of IEEE Asilomar Conference on Signals, Systems, and Computers, pp. 1398–1402.
32. Wang, Z. and Simoncelli, E. (2005) Translation insensitive image similarity in complex wavelet domain. Proceedings of International Conference on Acoustics, Speech, and Signal Processing, pp. 573–576.
33. Yang, C., Gao, W. and Po, L. (2008) Discrete wavelet transform-based structural similarity for image quality assessment. Proceedings of International Conference on Image Processing, pp. 377–380.
34. Wang, Z. and Li, Q. (2011) Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing, 20 (5), 1185–1198.
35. Seshadrinathan, K. and Bovik, A. (2007) A structural similarity metric for video based on motion models. Proceedings of International Conference on Acoustics, Speech, and Signal Processing, pp. 1869–1872.
36. Ninassi, A., Meur, O., Callet, P. and Barba, D. (2009) Considering temporal variations of spatial visual distortions in video quality assessment. IEEE Journal of Selected Topics in Signal Processing, 3 (2), 253–265.
37. Moorthy, A. and Bovik, A. (2010) Efficient video quality assessment along temporal trajectories. IEEE Transactions on Circuits System Video Technology, 20 (11), 1653–1658.
38. Video Quality Expert Group(2003)Final report from the Video Quality Experts Group on the validation of objective models of video quality assessment II.
39. Itti, L., Koch, C. and 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.
40. Rajashekar, U., Bovik, A.C. and Cormack, L.K. (2008) Gaffe: a gaze-attentive fixation finding engine. IEEE Transactions on Image Processing, 17 (4), 564–573.
41. Moorthy, A.K. and Bovik, A.C. (2009) Visual importance pooling for image quality assessment. IEEE Journal of Selected Topics in Signal Processing, Special Issue on Visual Media Quality Assessment, 3 (2), 193–220.
42. Sheikh, H., Seshadrinathan, K., Moorthy, A. et al.(2012)Image and video quality assessment research at LIVE, [Online]. Available: http://live.ece.utexas.edu/research/quality/.
43. David, H.A. (2003) Order Statistics, Wiley, New York.
44. Wang, Z. and Shang, X. (2006) Spatial pooling strategies for perceptual image quality assessment. Proceedings of IEEE International Conference on Image Processing, pp. 2945–2948.
45. Longere, P., Zhang, X., Delahunt, P. and Brainaro, D. (2002) Perceptual assessment of demosaicing algorithm performance. Proceedings of IEEE, 90 (1), 123–132.
46. Zeng, B. and Venetsanopoulos, A. (1993) A JPEG-based interpolative image coding scheme. Proceedings of International Conference on Acoustics, Speech, and Signal Processing, 5, pp. 393–396.
47. Lin, W. and Dong, L. (2006) Adaptive downsampling to improve image compression at low bit rates. IEEE Transactions on Image Processing, 15 (9), 2513–2521.
48. Ribas-Corbera, J. and Lei, S. (1999) Rate control in DCT video coding for low-delay communications. IEEE Transactions on Circuits System Video Technology, 9 (1), 172–185.
49. Li, Z.G., Gao, W., Pan, F. et al.(2006) Adaptive rate control for H.264. Journal of Visual Communication and Image Representation, 17, 376–406.
50. Liu, A. and Lin, W. (2009) Perception based down sampling for low bit rate image coding. Proceedings of International Pacific-Rim Conference on Multimedia: Advances in Multimedia Information Processing, pp. 212–221.
51. Rubinstein, M., Gutierrez, D., Sorkine, O. and Shamir., A. (2010) A comparative study of image retargeting. Proceedings of Association for Computing Machinery (ACM) Siggraph ASIA, p. 160.
52. Vaquero, D., Turk, M., Pulli, K. et al.(2010) A survey of image retargeting techniques. proceedings of SPIE Applications of Digital Image Processing XXXIII, 7798, p. 779814.
53. Avidan, S. and Shamir, A. (2007) Seam carving for content-aware image resizing. Association for Computing Machinery (ACM) Transactions on Graphics, 26 (3), Article No. 10.
54. Rubinstein, M., Shamir, A. and Avidan, S. (2008) Improved seam carving for video retargeting. Association for Computing Machinery (ACM) Transactions on Graphics, 27 (3), Article No. 16.
55. Achanta, R. and Susstrunk, S. (2009) Saliency detection for content-aware image resizing. Proceedings of IEEE International Conference on Image Processing.
56. Wolf, L., Guttmann, M. and Cohen-OR, D. (2007) Non-homogeneous content-driven video retargeting. Proceedings of IEEE International Conference on Computer Vision, pp. 1–6.
57. Ren, T., Liu, Y. and Wu, G. (2009) Image retargeting based on global energy optimization. IEEE International Conference on Multimedia and Expo, pp. 406–409.
58. Rubinstein, M., Shamir, A. and Avidan, S. (2009) Multioperator media retargeting. Association for Computing Machinery (ACM)Transaction on Graphics, 28 (3), 1–11.
59. Dong, W., Zhou, N., Paul, J.C. and Zhang, X. (2009) Optimized image resizing using seam carving and scaling. Association for Computing Machinery (ACM) Transaction on Graphics, 28 (5), 1–10.
60. Fang, Y., Chen, Z., Lin, W. and Lin, C. (2012) Saliency detection in the compressed domain for adaptive image retargeting. IEEE Transactions on Image Processing, 21 (9) 3888–3901.
61. Grundmann, M., Kwatra, V., Han, M. and Essa, I. (2010) Discontinuous seam-carving for video retargeting. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 569–576.
62. Jin, Y., Liu, L. and Wu, Q. (2010) Nonhomogeneous scaling optimization for realtime image resizing. The Visual Computer, 26 (6) 769–778,
63. Guo, Y., Liu, F., Shi, J. et al.(2009) Image retargeting using Mesh Parametrization. IEEE Transactions on Multimedia, 11 (5), 856–867.
64. Otsu, N. (1979) A threshold selection method from gray level histograms. IEEE Transactions on System, Man and Cybernetics, 9 (1), 62–66.
65. Macaire, L., Vandenbroucke, N. and Postaire, J.G. (2006) Color image segmentation by analysis of subset connectedness and color homogeneity properties. Computer Vision and Image Understanding, 102 (1), 105–116.
66. Pritch, Y., Kav-Venaki, E. and Peleg, S. (2009) Shift-map image editing. Proceedings of IEEE International Conference on Computer Vision, pp. 151–158.
67. Candès, E., Romberg, J. and Tao, T. (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52 (2), 489–509.
68. Candès, E. and Tao, T. (2006) Near optimal signal recovery from random projections: universal encoding strategies? IEEE Transactions on Information Theory, 52 (12), 5406–5425.
69. Donoho, D. (2006) Compressed sensing. IEEE Transactions on Information Theory, 52 (4), 1289–1306.
70. Candès, E. and Romberg, J. (2007) Sparsity and incoherence in compressive sampling. Inverse Problems, 23 (3), 969–985.
71. Candès, E. and Wakin, M. (2008) An introduction to compressive sampling. IEEE Signal Processing Magazine, Special Issue on Compressive Sampling, 21–30.
72. Baraniuk, R.G. (2007) Compressive sensing. Lecture Notes, IEEE Signal Processing Magazine, 24 (4) 118–124.
73. Candès, E. and Tao, T. (2005) Decoding by linear programming. IEEE Transactions on Information Theory, 51 (12), 4203–4215.
74. Chen, S.S., Donoho, D.L. and Saunders, M.A. (2001) Atomic decomposition by basis pursuit. SIAM Review, 43 (1), 129–159.
75. Tropp, J. and Gilbert, A.C. (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53 (12), 4655–4666.
76. Figueriredo, M., Nowak, R.D. and Wright, S.J. (2007) Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing, 1 (4), 586–597.
77. Wakin, M.B., Laska, J.N., Duarte, M.F. et al.(2006) Compressive imaging for video representation and coding. Proceedings of the Picture Coding Symposiu.
78. Duarte, M.F., Davenport, M.A., Takhar, D. et al.(2008) Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine, 25 (2), 83–91.
79. Herman, A. and Strohmer, T. (2009) High-resolution radar via compressed sensing. IEEE Transactions on Signal Processing, 57 (6), 2275–2284.
80. Lustig, G., Donoho, D.L. and Danly, J.M. (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine, 58, 1182–1195.
81. Stankovic, V., Stankovic, L. and Cheng, S. (2008) Compressive video sampling. Proceedings of 16th European Signal Processing Conference (EUSIPCO 2008).
82. Yu, Y., Wang, B. and Zhang, L. (2011) Saliency-based compressive sampling for image signals. IEEE Signal Processing Letters, 17 (11), 973–976.
83. Donoho, D.L. and Huo, X. (2001) Uncertainty principles and ideal atomic decomposition. IEEE Transactions on Information Theory, 47 (7), 2845–2862.
84. Candès, E., Romberg, J. and Tao, T. (2006) Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59 (8), 1207–1223.
85. Baraniuk, R., Davenport, M., DeVore, R. and Wakin, M. (2008) A simple proof of the restricted isometry property for random matrices. Constructive Approximation, 28 (3), 253–263.
86. Romberg, J. (2008) Imaging via compressive sampling. IEEE Signal Processing Magazine, 25 (2), 14–20.
87. Bruce, N.D. and Tsotsos, J.K. (2006)Saliency based on information maximization, Advances in Neural Information Processing Systems,18, 155–162, Also http://www-sop.inria.fr/members/Neil.Bruce/#SOURCECODE.