Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Joint successive correlation estimation and side information refinement in distributed video coding

This paper presents a novel hash-based distributed video coding (DVC) scheme that combines an accurate online correlation channel estimation (CCE) algorithm with an efficient side information refinement strategy, delivering state-of-the-art compression performance. The proposed DVC scheme applies layered bit-plane Wyner-Ziv coding and successively refines the CCE bit-plane-per-bit-plane during decoding. In addition, the side information is successively refined upon decoding of distinct refinement levels, grouping specific frequency bands of the discrete cosine transform. The proposed system not only outperforms the benchmark in DVC but several state-of-the-art side information refinement techniques and CCE methods as well

Side Information Generation in Distributed Video Coding

Distributed Video Coding (DVC) coding paradigm is based largely on two theorems of Information Theory and Coding, which are Slepian-wolf theorem and Wyner-Ziv theorem that were introduced in 1973 and 1976 respectively. DVC bypasses the need of performing Motion Compensation (MC) and Motion Estimation (ME) which are largely responsible for the complex encoder in devices. DVC instead relies on exploiting the source statistics, totally/partially, at only the decoder. Wyner-Ziv coding, a particular case of DVC, which is explored in detail in this thesis. In this scenario, two correlated sources are independently encoded, while the encoded streams are decoded jointly at the single decoder exploiting the correlation between them. Although the distributed coding study dates back to 1970’s, but the practical efforts and developments in the field began only last decade. Upcoming applications (like those of video surveillance, mobile camera, wireless sensor networks) can rely on DVC, as they don’t have high computational capabilities and/or high storage capacity. Current coding paradigms, MPEG-x and H.26x standards, predicts the frame by means of Motion Compensation and Motion Estimation which leads to highly complex encoder. Whilst in WZ coding, the correlation between temporally adjacent frames is performed only at the decoder, which results in fairly low complex encoder. The main objective of the current thesis is to investigate for an improved scheme for Side Information (SI) generation in DVC framework. SI frames, available at the decoder are generated through the means of Radial Basis Function Network (RBFN) neural network. Frames are estimated from decoded key frames block-by-block. RBFN network is trained offline using training patterns from different frames collected from standard video sequences

Distributed video coding with feedback channel constraints

Many of the distributed video coding (DVC) systems described in the literature make use of a feedback channel from the decoder to the encoder to determine the rate. However, the number of requests through the feedback channel is often high, and as a result the overall delay of the system could be unacceptable in practical applications. As a solution, feedback-free DVC systems have been proposed, but the problem with these solutions is that they incorporate a difficult trade-off between encoder complexity and compression performance. Recognizing that a limited form of feedback may be supported in many video-streaming scenarios, in this paper we propose a method for constraining the number of feedback requests to a fixed maximum number of N requests for an entire Wyner-Ziv (WZ) frame. The proposed technique estimates the WZ rate at the decoder using information obtained from previously decoded WZ frames and defines the N requests by minimizing the expected rate overhead. Tests on eight sequences show that the rate penalty is less than 5% when only five requests are allowed per WZ frame (for a group of pictures of size four). Furthermore, due to improvements from previous work, the system is able to perform better than or similar to DISCOVER even when up to two requests per WZ frame are allowed. The practical usefulness of the proposed approach is studied by estimating end-to-end delay and encoder buffer requirements, indicating that DVC with constrained feedback can be an important solution in the context of video-streaming scenarios

ADAPTIVE CHANNEL AND SOURCE CODING USING APPROXIMATE INFERENCE

Channel coding and source coding are two important problems in communications. Although both channel coding and source coding (especially, the distributed source coding (DSC)) can achieve their ultimate performance by knowing the perfect knowledge of channel noise and source correlation, respectively, such information may not be always available at the decoder side. The reasons might be because of the time−varying characteristic of some communication systems and sources themselves, respectively. In this dissertation, I mainly focus on the study of online channel noise estimation and correlation estimation by using both stochastic and deterministic approximation inferences on factor graphs.In channel coding, belief propagation (BP) is a powerful algorithm to decode low−density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. To solve the problem, two common workarounds in approximate inference are stochastic methods (e.g. particle filtering (PF)) and deterministic methods (e.g. expectation approximation (EP)). Generally, deterministic methods are much faster than stochastic methods. In contrast, stochastic methods are more flexible and suitable for any distribution. In this dissertation, I proposed two adaptive LDPC decoding schemes, which are able to perform online estimation of time−varying channel state information (especially signal to noise ratio (SNR)) at the bit−level by incorporating PF and EP algorithms. Through experimental results, I compare the performance between the proposed PF based and EP based approaches, which shows that the EP based approach obtains the comparable estimation accuracy with less computational complexity than the PF based method for both stationary and time−varying SNR, and enhances the BP decoding performance simultaneously. Moreover, the EP estimator shows a very fast convergence speed, and the additional computational overhead of the proposed decoder is less than 10% of the standard BP decoder.Moreover, since the close relationship between source coding and channel coding, the proposed ideas are extended to source correlation estimation. First, I study the correlation estimation problem in lossless DSC setup, where I consider both asymmetric and non−asymmetric SW coding of two binary correlated sources. The aforementioned PF and EP based approaches are extended to handle the correlation between two binary sources, where the relationship is modeled as a virtual binary symmetric channel (BSC) with a time−varying crossover probability. Besides, to handle the correlation estimation problem of Wyner−Ziv (WZ) coding, a lossy DSC setup, I design a joint bit−plane model, by which the PF based approach can be applied to tracking the correlation between non−binary sources. Through experimental results, the proposed correlation estimation approaches significantly improve the compression performance of DSC.Finally, due to the property of ultra−low encoding complexity, DSC is a promising technique for many tasks, in which the encoder has only limited computing and communication power, e.g. the space imaging systems. In this dissertation, I consider a real−world application of the proposed correlation estimation scheme on the onboard low−complexity compression of solar stereo images, since such solutions are essential to reduce onboard storage, processing, and communication resources. In this dissertation, I propose an adaptive distributed compression solution using PF that tracks the correlation, as well as performs disparity estimation, at the decoder side. The proposed algorithm istested on the stereo solar images captured by the twin satellites systemof NASA’s STEREO project. The experimental results show the significant PSNR improvement over traditional separate bit−plane decoding without dynamic correlation and disparity estimation

Ein Beitrag zur Pixel-basierten Verteilten Videocodierung: Seiteninformationsgenerierung, WZ-Codierung und flexible Decodierung

Moderne Anwendungsszenarien, wie die individuelle Übertragung von Videodaten zwischen mobilen Endgeräten, stellen neue Herausforderungen an das Videoübertragungssystem. Hierbei liegt ein besonderer Fokus auf der geringen Komplexität des Videoencoders. Diese Anforderung kann mit Hilfe der Verteilten Videocodierung erfüllt werden. Im Fokus der vorliegenden Arbeit liegen die sehr geringe Encoderkomplexität sowie auch die Steigerung der Leistungsfähigkeit und die Verbesserung der Flexibilität des Decodierungsprozesses. Einer der wesentlichen Beiträge der Arbeit bezieht sich auf die Verbesserung der Seiteninformationsqualität durch temporale Interpolation

Centralized and distributed semi-parametric compression of piecewise smooth functions

This thesis introduces novel wavelet-based semi-parametric centralized and distributed compression methods for a class of piecewise smooth functions. Our proposed compression schemes are based on a non-conventional transform coding structure with simple independent encoders and a complex joint decoder. Current centralized state-of-the-art compression schemes are based on the conventional structure where an encoder is relatively complex and nonlinear. In addition, the setting usually allows the encoder to observe the entire source. Recently, there has been an increasing need for compression schemes where the encoder is lower in complexity and, instead, the decoder has to handle more computationally intensive tasks. Furthermore, the setup may involve multiple encoders, where each one can only partially observe the source. Such scenario is often referred to as distributed source coding. In the first part, we focus on the dual situation of the centralized compression where the encoder is linear and the decoder is nonlinear. Our analysis is centered around a class of 1-D piecewise smooth functions. We show that, by incorporating parametric estimation into the decoding procedure, it is possible to achieve the same distortion- rate performance as that of a conventional wavelet-based compression scheme. We also present a new constructive approach to parametric estimation based on the sampling results of signals with finite rate of innovation. The second part of the thesis focuses on the distributed compression scenario, where each independent encoder partially observes the 1-D piecewise smooth function. We propose a new wavelet-based distributed compression scheme that uses parametric estimation to perform joint decoding. Our distortion-rate analysis shows that it is possible for the proposed scheme to achieve that same compression performance as that of a joint encoding scheme. Lastly, we apply the proposed theoretical framework in the context of distributed image and video compression. We start by considering a simplified model of the video signal and show that we can achieve distortion-rate performance close to that of a joint encoding scheme. We then present practical compression schemes for real world signals. Our simulations confirm the improvement in performance over classical schemes, both in terms of the PSNR and the visual quality

Fourth Workshop on Information Theoretic Methods in Science and Engineering : Proceedings

Peer reviewe