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

Distributed coding of endoscopic video

Triggered by the challenging prerequisites of wireless capsule endoscopic video technology, this paper presents a novel distributed video coding (DVC) scheme, which employs an original hash-based side-information creation method at the decoder. In contrast to existing DVC schemes, the proposed codec generates high quality side-information at the decoder, even under the strenuous motion conditions encountered in endoscopic video. Performance evaluation using broad endoscopic video material shows that the proposed approach brings notable and consistent compression gains over various state-of-the-art video codecs at the additional benefit of vastly reduced encoding complexity

Intra-WZ quantization mismatch in distributed video coding

During the past decade, Distributed Video Coding (DVC) has emerged as a new video coding paradigm, shifting the complexity from the encoder-to the decoder-side. This paper addresses a problem of current DVC architectures that has not been studied in the literature so far, that is, the mismatch between the intra and Wyner-Ziv (WZ) quantization processes. Due to this mismatch, WZ rate is spent even for spatial regions that are accurately approximated by the side-information. As a solution, this paper proposes side-information generation using selective unidirectional motion compensation from temporally adjacent WZ frames. Experimental results show that the proposed approach yields promising WZ rate gains of up to 7% relative to the conventional method

Low complexity video compression using moving edge detection based on DCT coefficients

In this paper, we propose a new low complexity video compression method based on detecting blocks containing moving edges us- ing only DCT coe±cients. The detection, whilst being very e±cient, also allows e±cient motion estimation by constraining the search process to moving macro-blocks only. The encoders PSNR is degraded by 2dB com- pared to H.264/AVC inter for such scenarios, whilst requiring only 5% of the execution time. The computational complexity of our approach is comparable to that of the DISCOVER codec which is the state of the art low complexity distributed video coding. The proposed method ¯nds blocks with moving edge blocks and processes only selected blocks. The approach is particularly suited to surveillance type scenarios with a static camera

Statistical framework for video decoding complexity modeling and prediction

Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time - feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding