article

Improved estimation for just-noticeable visual distortion

Authors:

P. XueAuthors Info & Claims

Signal Processing, Volume 85, Issue 4

Pages 795 - 808

https://doi.org/10.1016/j.sigpro.2004.12.002

Published: 01 April 2005 Publication History

Abstract

Perceptual visibility threshold estimation, based upon characteristics of the human visual system (HVS), has wide applications in digital image/video processing. An improved scheme for estimating just-noticeable distortion (JND) is proposed in this paper. It is proved to outperform the DCTune model, with the major contributions of a new formula for luminance adaptation adjustment and the incorporation of block classification for contrast masking. The HVS visibility threshold for digital images exhibits an approximately parabolic curve versus gray levels and this has been formulated to yield a more accurate base threshold. Moreover, edge regions have been differentiated via block classification to effectively avoid over-estimation of JND in the said regions. Experiments with different images and the associated subjective tests show improved performance of the proposed scheme over the DCTune model for luminance adaptation (especially in dark regions) and masking effect in edge regions. Our model has further demonstrated to achieve favorable results in perceptual visual distortion gauge and image compression. The improvement in JND estimation facilitates better visual distortion measurement and visual signal compression.

References

[1]

{1} A.J. Ahumada, H.A. Peterson, Luminance-model-based DCT quantization for color image compression, Hum. Vision Visual Process. Digital Display III (1992) 365-374.

[2]

{2} Y.J. Chiu, T. Berger, A software-only videocodec using pixelwise conditional differential replenishment and perceptual enhancement, IEEE Trans. Circuits Systems Video Technol. 9 (3) (April 1999) 438-450.

Digital Library

[3]

{3} C.H. Chou, Y.C. Li, A perceptually tuned subband image coder based on the measure of Just-Noticeable-Distortion profile, IEEE Trans. Circuits Systems Video Technol. 5 (6) (December 1995) 467-476.

Digital Library

[4]

{4} S. Daly, The visible differences predictor: an algorithm for the assessment of image fidelity, in: A.B. Watson (Ed.), Digital Images and Human Vision, The MIT Press, Cambridge, 1993, pp. 179-205.

Digital Library

[5]

{5} A.M. van Dijk, J. Martens, Subjective quality assessment of compressed images, Signal Process. 58 (1997) 235-252.

Digital Library

[6]

{6} M.P. Eckert, A.P. Bradley, Perceptual quality metrics applied to still image compression, Signal Process. 70 (1998) 177-200.

Digital Library

[7]

{7} B. Girod, What's wrong with mean-squared error?, in: A.B. Watson (Ed.), Digital Images and Human Vision, The MIT Press, Cambridge, 1993, pp. 207-220.

Digital Library

[8]

{8} R.C. Gonzalez, R.E. Woods, Digital Image Processing, second ed., Prentice-Hall, Englewood Cliffs, 2002, pp. 484-499.

[9]

{9} P.J. Hahn, V.J. Mathews, An analytical model of the perceptual threshold function for multichannel image compression, IEEE International Conference on Image Processing (ICIP'98), vol. 3, October 1998, pp. 404-408.

[10]

{10} I. Hontsch, L.J. Karam, Locally adaptive perceptual image coding, IEEE Trans. Image Process. 9 (9) (September 2000) 1472-1483.

Digital Library

[11]

{11} I. Hontsch, L.J. Karam, Adaptive image coding with perceptual distortion control, IEEE Trans. Image Process. 11 (3) (March 2002) 213-222.

Digital Library

[12]

{12} N. Jayant, J. Johnston, R. Safranek, Signal compression based on models of human perception, in: Proceedings of the IEEE, October 1993, pp. 1385-1422.

[13]

{13} D.H. Kelly, Visual processing of moving stimuli, J. Opt. Soc. Am. A 2 (1985) 216-225.

[14]

{14} G.E. Legge, A power law for contrast discrimination, Vision Res. 21 (1981) 457-467.

[15]

{15} G.E. Legge, J.M. Foley, Contrast masking in human vision, J. Opt. Soc. Am. 70 (1980) 1458-1471.

[16]

{16} P. Longere, X. Zhang, P.B. Delahunt, D.H. Brainaro, Perceptual assessment of demosaicing algorithm performance, Proc. IEEE 90 (7) (January 2002) 123-132.

[17]

{17} M.J. Nadenau, Integration of human color vision models into high quality image compression, Ph.D. Thesis, Lausanne, EPFL 2000.

[18]

{18} F.L. van Nes, M.A. Bouman, Spatial modulation transfer in the human eye, J. Opt. Soc. Am. 57 (1967) 401-406.

[19]

{19} A.N. Netravali, B.G. Haskell, Digital Pictures: Representation and Compression, Plenum, New York, 1988.

[20]

{20} T.N. Pappas, R.J. Safranek, Perceptual criteria for image quality evaluation, in: A.C. Bovik (Ed.), Handbook of Image and Video Processing, Academic Press, New York, 2000.

[21]

{21} J. Park, J.M. Jo, J. Jeong, Some adaptive quantizers for HDTV image compression, in: L. Stenger et al. (Eds.), Signal Processing of HDTV, vol. V, 1994.

[22]

{22} H.A. Peterson, A.J. Ahumuda, A.B. Watson, An improved detection model for DCT coefficient quantization, Hum. Vision Visual Process. Digital Display VI (1993) 191-201.

[23]

{23} H.A. Peterson, H. Peng, J.H. Morgan, W.B. Pennebaker, Quantization of color image components in the DCT domain, in: B.E. Rogowitz, M.H. Brill, J.P. Allebach (Eds.), Human Vision, Visual Processing, and Digital Display II, Proc. SPIE, vol. 1453, 1991, pp. 210-222.

[24]

{24} J.G. Robson, N. Graham, Probability summation and regional variation in contrast sensitivity across the visual field, Vision Res. 21 (1981) 409-418.

[25]

{25} R.J. Safranek, J.D. Johnston, A perceptually tuned subband image coder with image dependent quantization and post-quantization data compression, in: Proceedings of the IEEE ICASSP, 1989, pp. 1945-1948.

[26]

{26} Sarnoff Corporation, JND: A human vision system model for objective picture quality measurements, White paper, June 2001. http://www.sarnoff.com/products_services/ video_vision/jndmetrix/documents/index.asp.

[27]

{27} Sarnoff Corporation, Measuring Image Quality: Sarnoff's JNDmetrix Technology, Sarnoff JNDmetrix Technology Overview, July 2002.

[28]

{28} W. Schreiber, Fundamentals of Electronic Imaging Systems, Springer, New York, 1993.

[29]

{29} H.Y. Tong, A.N. Venetsanopoulos, A perceptual model for JPEG applications based on block classification, texture masking, and luminance masking, IEEE International Conference on Image Processing (ICIP'98), Chicago, October 1998, pp. 428-432.

[30]

{30} T.D. Tran, R. Safranek, A locally adaptive perceptual masking threshold model for image coding, in: Proceedings ICASSP-96, vol. 4, 1996, pp. 1883-1886.

[31]

{31} VQEG (Video Quality Expert Group), Evaluation of new methods for objective testing of video quality: objective test plan, ITU-T/COM-T/COM12/C, www.vqeg.org, September 1998.

[32]

{32} A.B. Watson, The cortex transform: rapid computation of simulated neural images, Comput. Vision Graphics Image Process. 39 (1987) 311-327.

Digital Library

[33]

{33} A.B. Watson, DCTune: a technique for visual optimization of DCT quantization matrices for individual images, Soc. Inf. Display Digest Tech. Papers XXIV (1993) 946-949.

[34]

{34} A.B. Watson, G.Y. Yang, J.A. Solomon, J. Villasenor, Visibility of wavelet quantization noise, IEEE Trans. Image Process. 6 (1997) 1164-1175.

Digital Library

[35]

{35} R.B. Wolfgang, C.I. Podilchuk, E.J. Delp, Perceptual watermarks for digital images and video, Proc. IEEE 87 (7) (1999).

Cited By

Liao LXu KWu HChen CSun WYan QJay Kuo CLin W(2024)Blind Video Quality Prediction by Uncovering Human Video Perceptual RepresentationIEEE Transactions on Image Processing10.1109/TIP.2024.344573833(4998-5013)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3445738
Shen ZChen CZhang RYu HLi L(2024)Perception-JND-driven path tracing for reducing sample budgetThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-03199-w40:11(7651-7665)Online publication date: 1-Nov-2024
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Xie WWang STian SHuang LLiu YWang MWilliams BChen YNeville J(2023)Just noticeable visual redundancy forecastingProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i3.25399(2965-2973)Online publication date: 7-Feb-2023
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Index Terms

Improved estimation for just-noticeable visual distortion
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections
  2. Computer graphics
    1. Image compression

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Published In

cover image Signal Processing

Signal Processing Volume 85, Issue 4

April 2005

206 pages

ISSN:0165-1684

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Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 April 2005

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Cited By

Liao LXu KWu HChen CSun WYan QJay Kuo CLin W(2024)Blind Video Quality Prediction by Uncovering Human Video Perceptual RepresentationIEEE Transactions on Image Processing10.1109/TIP.2024.344573833(4998-5013)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3445738
Shen ZChen CZhang RYu HLi L(2024)Perception-JND-driven path tracing for reducing sample budgetThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-03199-w40:11(7651-7665)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s00371-023-03199-w
Xie WWang STian SHuang LLiu YWang MWilliams BChen YNeville J(2023)Just noticeable visual redundancy forecastingProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i3.25399(2965-2973)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i3.25399
Zhang YGong YWang JSun JWan W(2023)Towards perceptual image watermarking with robust texture measurement▪Expert Systems with Applications: An International Journal10.1016/j.eswa.2023.119649219:COnline publication date: 1-Jun-2023
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Wan WHuang DShang BWei SWu HWu JShi G(2022)Depth Perception Assessment of 3D Videos Based on Stereoscopic and Spatial Orientation Structural FeaturesIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.316597033:9(4588-4602)Online publication date: 8-Apr-2022
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Hu HLu YLee T(2022)Blind image watermarking via psychovisual-based relative modulation in DCT domain with performance optimized by GWOMultimedia Tools and Applications10.1007/s11042-022-12645-181:15(21675-21695)Online publication date: 1-Jun-2022
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Tian TWang HKwong SKuo C(2021)Perceptual Image Compression with Block-Level Just Noticeable Difference PredictionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/340832016:4(1-15)Online publication date: 28-Jan-2021
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Shen XNi ZYang WZhang XWang SKwong S(2020)Just Noticeable Distortion Profile Inference: A Patch-Level Structural Visibility Learning ApproachIEEE Transactions on Image Processing10.1109/TIP.2020.302942830(26-38)Online publication date: 18-Nov-2020
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Deng JWang CLiu S(2020)No Reference Image Quality Assessment by Information DecompositionMultiMedia Modeling10.1007/978-3-030-37731-1_67(826-838)Online publication date: 5-Jan-2020
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Prangnell LSanchez V(2018)JND-Based Perceptual Video Coding for 4:4:4 Screen Content Data in HEVC2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2018.8462327(1203-1207)Online publication date: 15-Apr-2018
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