Abstract
JPEG is wildly used for image compression, which inevitably introduces some distortions, such as blocking artifacts and blurring. Peak Signal to Noise Ratio (PSNR) is the most widely used objective criterion to evaluate image distortion, which is a full reference image quality assessment and requires original image as the reference. However, this requirement cannot always be guaranteed, so that no reference PSNR estimate (NRPE) is required in some applications. NRPE is an ill-pose problem and need some prior knowledge to produce rational results. DCT coefficients are usually assumed with even or Gaussian distributions, and their parameters are estimated by learning or no learning based algorithms in PSNR calculation. These works are unsatisfied for their estimate error is even larger than 3 dB for the heavy compressed images. Note that the correlations of image pixels will be destroyed and some artifacts will appear after heavy compression, such as blocking and blurring. In this paper, the relationship of mean squared difference of slope (MSDS), pixel correlation, image variance and the left alternating current (AC) energy is theoretically analyzed, and then PSNR is constructed as the function of MSDS and left AC energy. The left AC energy cannot be exactly measured in decoded image, hence that it is replaced by the index of the last nonzero coefficients for simplicity. Benefit from this arrangement, the proposed algorithm produces more accurate results over the-state-of-art NRPE algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Turaga, D.S., Chen, Y., Caviedes, J.: No reference PSNR estimation for compressed pictures. Signal Process. Image Commun. 19, 173–184 (2004)
Knee, M.: A single-ended picture quality measure for MPEG-2. In: Proceedings of International Broad. Convention (IBC 2000), pp. 95–100, September 2000
Knee, M., Diggins, M.J.: World Intellectual Property Bureau, Improvements in Data Compression. International Patent Appl.WO00/22 834 (2000)
Ichigaya, A., Kurozumi, M., Hara, N., Nishida, Y., Nakasu, E.: A method of estimating coding PSNR using quantized DCT coefficients. IEEE Trans. Circuits Syst. Video Technol. 16, 251–259 (2006)
Wang, C., Dong, H.Y., Wu, Z.K., Tan, Y.P.: Example-based objective quality estimation for compressed images. IEEE Multimedia 17(3), 54–61 (2010)
Yang, G., Tan, Y.P.: Blind PSNR estimation using shifted blocks for JPEG images. IEEE Int. Symp. Circ. Syst. 19(5), 1235–1238 (2011)
Jayant, N.S., Noll, P.: Digital Coding of Waveforms. Prentice-Hall, Englewood Cliffs (1984)
Pao, I.M., Sun, M.T.: Modeling DCT coefficients for fast video encoding. IEEE Trans. Circ. Syst. Video Technol. 9, 608–616 (1999)
Brandao, T., Queluz, M.P.: No-reference image quality assessment based on DCT-domain statistics. Signal Process. 88(4), 822–833 (2008)
Robertson, M.A., Stevenson, R.L.: DCT quantization noise in compressed images. IEEE Trans. Circ. Syst. Video Technol. 15, 27–38 (2005)
Muller, F.: Distribution shape of two-dimensional DCT coefficients of natural images. Electron. Lett. 29(22), 1935–1936 (1993)
Eude, T., Grisel, R., Cherifi, H., Debrie, R.: On the distribution of the DCT coefficients. In: Proceedings of the IEEE International Conference on Acoustics, Speech Signal Processing, vol. 5, pp. 365–368 (1994)
Chang, J.-H., Shin, J.W., Kim, N.S., Mitra, S.: Image probability distribution based on generalized gamma function. IEEE Signal Process. Lett. 12(4), 325–328 (2005)
Lam, E., Goodman, J.: A mathematical analysis of the DCT coefficient distributions for images. IEEE Trans. Image Process. 9(10), 1661–1666 (2000)
Eggerton, J., Srinath, M.: Statistical distributions of image DCT coefficients. Comput. Electr. Eng. 12(3–4), 137–145 (1986)
Altunbasak, Y., Kamaci, N.: An analysis of the DCT coefficient distribution with the H.264 video coder. In: Proceedings of the IEEE International Conference Acoustics, Speech and Signal Process., vol. 3, pp. 177–180, May 2004
Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: Live image quality assessment database release 2. http://live.ece.utexas.edu/research/quality
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Wang, C., Yang, Y., Shen, J. (2018). PSNR Estimate for JPEG Compression. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_68
Download citation
DOI: https://doi.org/10.1007/978-3-319-77383-4_68
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77382-7
Online ISBN: 978-3-319-77383-4
eBook Packages: Computer ScienceComputer Science (R0)