No-reference image quality assessment based on DCT domain statistics
Signal Processing
Evaluating a feedback channel based transform domain Wyner-Ziv video codec
Image Communication
Accurate Correlation Modeling for Transform-Domain Wyner-Ziv Video Coding
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Distributed Source Coding: Theory, Algorithms and Applications
Distributed Source Coding: Theory, Algorithms and Applications
Improved virtual channel noise model for transform domain Wyner-Ziv video coding
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Dynamic quality control for transform domain Wyner-Ziv video coding
Journal on Image and Video Processing - Special issue on distributed video coding
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Adaptive correlation estimation for general Wyner-Ziv video coding
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
The rate-distortion function for source coding with side information at the decoder
IEEE Transactions on Information Theory
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
Correlation Noise Modeling for Efficient Pixel and Transform Domain Wyner–Ziv Video Coding
IEEE Transactions on Circuits and Systems for Video Technology
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Aiming for low-complexity encoding, video coders based on Wyner-Ziv theory are still unsuccessfully trying to match the performance of predictive video coders. One of the most important factors concerning the coding performance of distributed coders is modeling and estimating the correlation between the original video signal and its temporal prediction generated at the decoder. One of the problems of the state-of-the-art correlation estimators is that their performance is not consistent across a wide range of video content and different coding settings. To address this problem we have developed a correlation model able to adapt to changes in the content and the coding parameters by exploiting the spatial correlation of the video signal and the quantization distortion. In this paper we describe our model and present experiments showing that our model provides average bit rate gains of up to 12% and average PSNR gains of up to 0.5dB when compared to the state-of-the-art models. The experiments suggest that the performance of distributed coders can be significantly improved by taking video content and coding parameters into account.