Quality Estimation for H.264/SVC Inter-layer Residual Prediction in Spatial Scalability

  • Ren-Jie Wang
  • Yan-Ting Jiang
  • Jiunn-Tsair Fang
  • Pao-Chi Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7088)

Abstract

Scalable Video Coding (SVC) provides an efficient compression for the video bitstream equipped with various scalable configurations. H.264 scalable extension (H.264/SVC) is the most recent scalable coding standard. It involves the state-of-the-art inter-layer prediction to provide higher coding efficiency than previous standards. Moreover, the requirements for the video quality on distinct situations like link conditions or video contents are usually different. Therefore, it is very desirable to be able to construct a model so that the target quality can be estimated in advance. This work proposes a Quantization-Distortion (Q-D) model for H.264/SVC spatial scalability, and then we can estimate video quality before the actual encoding is performed. In particular, we further decompose the residual from the inter-layer residual prediction into the previous distortion and Prior-Residual so that the residual can be estimated. In simulations, based on the proposed model, we estimate the actual Q-D curves, and its average accuracy is 88.79%.

Keywords

H.264 Scalable Video Coding Spatial Scalability Quality Estimation Quantization-Distortion Model 

References

  1. 1.
    Schwarz, H., Marpe, D., Wiegand, T.: Overview of the Scalable Video Coding Extension of the H.264/AVC Standard. IEEE Trans. Circuits Syst. Video Technol. 17(9), 1103–1120 (2007)CrossRefGoogle Scholar
  2. 2.
    Segall, A., Sullivan, G.J.: Spatial Scalability Within the H.264/AVC Scalable Video Coding Extension. IEEE Transactions on Circuits and Systems for Video Technology 17(9), 1121–1135 (2007)CrossRefGoogle Scholar
  3. 3.
    Turaga, D.S., Chen, Y., Caviedes, J.: No reference PSNR estimation for compressed pictures. Signal Process. Image Commun. 19, 173–184 (2004)CrossRefGoogle Scholar
  4. 4.
    Kamaci, N., Altinbasak, Y., Mersereau, R.M.: Frame bit allocation for the H.264/AVC video coder via Cauchy density-based rate and distortion models. IEEE Trans. Circuits Syst. Video Technol. 15(8), 994–1006 (2005)CrossRefGoogle Scholar
  5. 5.
    Berger, T.: Rate-Distortion Theory: A Mathematical Basis for Data Compression. Prentice-Hall, Englewood Cliffs (1971)MATHGoogle Scholar
  6. 6.
    Takagi, K., Takishima, Y., Nakajima, Y.: A study on rate distortion optimization scheme for JVT coder. In: Proc. SPIE, vol. 5150, pp. 914–923 (2003)Google Scholar
  7. 7.
    Wang, H., Kwong, S.: A rate-distortion optimization algorithm for rate control in H.264. In: Proc. IEEE ICASSP 2007, pp. 1149–1152 (April 2007)Google Scholar
  8. 8.
    Liu, J., Cho, Y., Guo, Z., Kuo, C.C.: Bit Allocation for Spatial Scalability Coding of H.264/SVC With Dependent Rate-Distortion Analysis. IEEE Trans. Circuits Syst. Video Technol. 20(7), 967–981 (2010)CrossRefGoogle Scholar
  9. 9.
    Hu, S.H., Wang, H., Kwong, S., Zhao, T., Kuo, C.C.: Rate Control Optimization for Temporal-Layer Scalable Video Coding. IEEE Trans. Circuits Syst. Video Technol. 21(8), 1152–1162 (2011)CrossRefGoogle Scholar
  10. 10.
    Guo, L., Au, O.C., Ma, M., Liang, Z., Wong, P.H.W.: A Novel Analytic Quantization-Distortion Model for Hybrid Video Coding. IEEE Trans. Circuits Syst. Video Technol. 19(5), 627–641 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ren-Jie Wang
    • 1
  • Yan-Ting Jiang
    • 1
  • Jiunn-Tsair Fang
    • 2
  • Pao-Chi Chang
    • 1
  1. 1.Dept. of Communication EngineeringNational Central Univ.JhongliTaiwan
  2. 2.Dept. of Electronic EngineeringMing Chuan Univ.TaoyuanTaiwan

Personalised recommendations