Abstract
The estimation of end-to-end distortion plays a key role in error-resilient video coding and perceptual quality control. The traditional end-to-end distortion estimation methods are mainly based on the MSE or MAD values, which sometimes poorly reflect subjective perception. This paper proposes a novel method to model the end-to-end quality degradation based on the SSIM index. Using factors extracted from the encoder, we build the models by considering the source distortion, the error-propagated distortion and the error-concealment distortion. These models can be used in joint source-channel coding with rate-distortion optimization as well as error-resilient video coding based on perception.
This reserch has been supported in part by National Natural Science Foundation of China: 61001177.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Stuhlmuller, K., Farber, N., Link, M., Girod, B.: Analysis of video transmission over lossy channels. IEEE J. Select. Areas Commun. 18, 1012–1032 (2000)
Wu, D., Hou, Y.T., Li, B., Zhu, W., Zhang, Y.-Q., Chao, H.J.: An end-to-end approach for optimal mode selection in Internet video communication: theory and application. IEEE J. Select. Areas Commun. 18(6), 977–995 (2000)
Zhang, R., Regunathan, S.L., Rose, K.: Video coding with optimal inter/intra-mode switching for packet loss resilience. IEEE J. Select. Areas Commun. 18(6), 966–976 (2000)
Zhang, Y., Gao, W., Lu, Y., Huang, Q., Zhao, D.: Joint source-channel rate-distortion optimization for H.264 video coding over error-prone networks. IEEE Trans. on Multimedia 9(3), 445–454 (2007)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. Image Processing 13(4), 600–612 (2004)
ATIS: Objective Perceptual Video Quality Measurement Using a JND Based Full Reference Technique. Alliance for Telecommunications Industry Solutions Technical Report, T1.TR. 75-2001 (2001)
Pinson, M., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Trans. on Broadcasting 50(3), 312–322 (2004)
Koumaras, H., Kourtis, A., Lin, C.-H., Shieh, C.-K.: A Theoretical Framework for End-to-End Video Quality Prediction of MPEG-based Sequences. In: Proc. 3rd Int’l Conf. on Networking and Services, Athens, Greece (2007)
Yim, C., Bovik, A.C.: Evaluation of temporal variation of video quality in packet loss networks. Signal Processing: Image Communication, 24–38 (2011)
Wang, Y., Lin, T.-L., Cosman, P.: Network-based model for video packet importance considering both compression artifacts and packet losses. In: IEEE Globecom 2010 (2010)
Ou, T.-S., Huang, Y.-H., Chen, H.H.: SSIM-Based Perceptual Rate Control for Video Coding. IEEE Trans. on Circuits and Systems for Video Technology 21(5) (2011)
Wang, S., Rehman, A., Wang, Z., Ma, S., Gao, W.: Rate-SSIM Optimization For Video Coding. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (2011)
McCullagh, P., Nelder, J.A.: Generalized Linear Models, 2nd edn. Chapman and Hall (1989)
Cui, Z., Zhu, X.: Subjective Quality Optimized Intra Mode Selection for H.264 I Frame Coding Based on SSIM. In: The Sixth International Conference on Image and Graphics (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Y., Zhang, Y., Lu, R., Cosman, P.C. (2012). SSIM-Based End-to-End Distortion Modeling for H.264 Video Coding. In: Lin, W., et al. Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34778-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-34778-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34777-1
Online ISBN: 978-3-642-34778-8
eBook Packages: Computer ScienceComputer Science (R0)