Abstract
In this paper, we propose a novel quality control scheme which aims to keep quality consistency within a frame. Quality consistency is an important requirement in video coding. However, many existing schemes usually consider the quality consistency as the quantization parameter (QP) consistency. Moreover, the most frequently used metric to evaluate the quality consistency is PSNR, which has been well known that it is not good for subjective quality evaluation. These flaws of the existing methods are pointed out and proved to be unreasonable. For optimization, we take the effect of texture complexity on subjective evaluation into consideration to build a new D-Q model. We use the new model to adjust the quantization parameters of different regions to keep quality consistency. The simulation result shows that the new scheme gets better subjective quality and higher coding efficiency compared to traditional way.
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References
Viterbi, A., Omura, J.: Principles of Digital Communication and Coding. McGraw-Hill, New York (1979)
Schuster, G.M., Katsaggelos, A.K.: Rate-Distortion Based Video Compression. Kluwer Academic Publishers, Norwell (1997)
Lin, L.J., Ortega, A.: Bit-rate control using piecewise approximated rate–distortion characteristics. IEEE Trans. Circuits and Systems for Video Technology 8, 446–459 (1998)
Chen, Z., Ngan, K.N.: Distortion variation minimization in real-time video coding. Signal Processing-Image Communication 21, 273–279 (2006)
Hong, S.H., Yoo, S.J., Lee, S.W., Kang, H.S., Hong, S.Y.: Rate control of MPEG video for consistent picture quality. IEEE Transactions on Broadcasting 49, 1–13 (2003)
Li, Z., Pan, F., Lim, K.P., Feng, G., Lin, X., Rahardja, S.: Adaptive basic unit layer rate control for JVT. JVT-G012 (2003)
Hoang, D.T., Linzer, E., Vitter, J.S.: Lexicographic bit allocation for MPEG video. Journal of Visual Communication and Image Representation 8, 384–404 (1997)
Bhat, A., Richardson, I., Kannangara, S.: A new perceptual quality metric for compressed video. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 933–936 (2009)
Girod, B.: What’s wrong with mean-squared error. In: Digital Images and Human Vision. MIT Press, Cambridge (1993)
Ran, X., Farvardin, N.: A perceptually motivated three-component image model – Part 1: Description of the model. IEEE Trans. on Image Processing 4, 401–415 (1995)
Jayant, N., Noll, P.: Digital Coding of Waveforms, Englewood Cliffs, NJ (1994)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Processing Letters 9, 81–84 (2002)
ITU-R BT.500 Methodology for the Subjective Assessment of the Quality for TV Pictures, ITU-R Std. (2002)
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© 2011 Springer-Verlag Berlin Heidelberg
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Yu, L., Dai, F., Zhang, Y., Lin, S. (2011). Perceptual Motivated Coding Strategy for Quality Consistency. In: Lee, KT., Tsai, WH., Liao, HY.M., Chen, T., Hsieh, JW., Tseng, CC. (eds) Advances in Multimedia Modeling. MMM 2011. Lecture Notes in Computer Science, vol 6523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17832-0_3
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DOI: https://doi.org/10.1007/978-3-642-17832-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17831-3
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