Correlation modeling with decoder-side quantization distortion estimation for distributed video coding

Aiming for low-complexity encoding, distributed video coders still fail to achieve the performance of current industrial standards for video coding. One of most important problems in this area is the accurate modeling of the correlation between the predicted signal and the original video. In our previous work we showed that exploiting the quantization distortion can significantly improve the accuracy of a correlation estimator. In this paper we describe how the quantization distortion can be exploited purely at the decoder side without any performance penalty when compared to an encoder-aided system. As a result, the proposed correlation estimator delivers state-of-the-art modeling accuracy while neatly fitting the low-encoder-complexity characteristic of distributed video coding.

[1]  F. Pereira,et al.  Evaluating a feedback channel based transform domain Wyner-Ziv video codec , 2008, Signal Process. Image Commun..

[2]  Xin Huang,et al.  Improved virtual channel noise model for transform domain Wyner-Ziv video coding , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Rik Van de Walle,et al.  Exploiting quantization and spatial correlation in virtual-noise modeling for distributed video coding , 2010, Signal Process. Image Commun..

[4]  Peter Lambert,et al.  Accounting for quantization noise in online correlation noise estimation for Distributed Video Coding , 2009, 2009 Picture Coding Symposium.

[5]  Oscar C. Au,et al.  Adaptive correlation estimation for general Wyner-Ziv video coding , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[6]  Tiago Rosa Maria Paula Queluz,et al.  No-reference image quality assessment based on DCT domain statistics , 2008, Signal Process..

[7]  Gary J. Sullivan,et al.  Efficient scalar quantization of exponential and Laplacian random variables , 1996, IEEE Trans. Inf. Theory.

[8]  Catarina Brites,et al.  Correlation Noise Modeling for Efficient Pixel and Transform Domain Wyner–Ziv Video Coding , 2008, IEEE Transactions on Circuits and Systems for Video Technology.