Exploiting quantization and spatial correlation in virtual-noise modeling for distributed video coding Jozef ˇ Skorupa a,Ã ,J¨ urgen Slowack a , Stefaan Mys a , Nikos Deligiannis b , Jan De Cock a , Peter Lambert a , Adrian Munteanu b , Rik Van de Walle a a Ghent University – IBBT, Department of Electronics and Information Systems – Multimedia Lab, Gaston Crommenlaan 8 bus 201, B-9050 Ledeberg-Ghent, Belgium b Vrije Universiteit Brussel – IBBT, Electronics and Informatics Department, Pleinlaan 2, B-1050 Brussels, Belgium article info Article history: Received 15 January 2010 Accepted 15 May 2010 Keywords: Context adaptive Distributed video coding Virtual-noise modeling abstract 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 0.5 dB 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. & 2010 Elsevier B.V. All rights reserved. 1. Introduction When Shannon published his work on fundamentals in communication theory in 1948 [1], he started a new era. Data compression was one of the affected fields which obtained a powerful mathematical abstraction as well as bounds for the optimal performance. Classically, the information to be compressed was available at one place, leading to centralized coding theory. Some 25 years later the idea was generalized by Slepian and Wolf [2], and Wyner and Ziv [3] to the distributed case, where the information to be compressed is distributed between several independent encoding terminals. Although this generalization was purely theoretical at the time, the problem of distributed compres- sion is gaining practical relevance nowadays, due to the advent of sensor and ad-hoc networks. Interestingly enough, under certain assumptions the compression in the distrib- uted case can achieve the same performance bounds as in the centralized case. This means that having the information to be compressed scattered over several independent places leads to no or a small penalty in the compression efficiency. A related topic, where distributed compression gains significant attention, is video compression. Here, the application is driven by the demand for low complexity encoding, suiting application scenarios with mobile or power-constrained encoding terminals. Current video Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/image Signal Processing: Image Communication 0923-5965/$ - see front matter & 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.image.2010.05.005 Ã Corresponding author. E-mail addresses: jozef.skorupa@ugent.be (J. ˇ Skorupa), jurgen.slowack@ugent.be (J. Slowack), stefaan.mys@ugent.be (S. Mys), ndeligia@etro.vub.ac.be (N. Deligiannis), jan.decock@ugent.be (J. De Cock), peter.lambert@ugent.be (P. Lambert), acmuntea@etro.vub.ac.be (A. Munteanu), rik.vandewalle@ugent.be (R. Van de Walle). Signal Processing: Image Communication 25 (2010) 674–686