Distributed Video Coding: Selecting the most promising application scenarios

Abstract

a b s t r a c tDistributed Video Coding (DVC) is a new video coding paradigm based on two major Information Theory results: the Slepian-Wolf and Wyner-Ziv theorems. Recently, practical DVC solutions have been proposed with promising results; however, there is still a need to study in a more systematic way the set of application scenarios for which DVC may bring major advantages. This paper intends to contribute for the identification of the most DVC friendly application scenarios, highlighting the expected benefits and drawbacks for each studied scenario. This selection is based on a proposed methodology which involves the characterization and clustering of the applications according to their most relevant characteristics, and their matching with the main potential DVC benefits.

Power Management in Sensing Subsystem of Wireless Multimedia Sensor Networks

“…For the second set of applications, clustering may serve as a baseline framework for collaborative node scheduling avoiding redundant sensing and processing and thus increasing network lifetime. Other applications that are interested in correlated data (e.g., Distributed Video Coding, DVC) may use clustering in order to exploit multi-view correlations to build joint encoders (Pereira et al, 2008). …”

Region Adaptive Measurements for Distributed Compressive Video Sensing

“…This is helpful for the WVSN. If DVC is introduced in WVSN, the sensor nodes can operate the video encoder with low complexity while the servers can perform the video decoder with high complexity [5]. Furthermore, in view of fast data acquisition, compressive sensing (CS) has drawn significant attention among industry and academic researchers [6].…”

Improving distributed video coding side information by intelligently combining macro-blocks from multiple algorithms

“…This enables relatively simple encoder designs which can be readily deployed in resource-constrained portable devices [3]. In DVC, selected frames (known as key frames) of a sequence are encoded using conventional encoding method such as H.264 at encoder and send to decoder through a communication channel.…”

“…Hence the quality of SI has a major impact on the resulting DVC output quality [4][5][6][7]. SI is commonly generated using linear-motion compensated temporal interpolation (LMCTI) [3][4][5] and while this generally provides reasonable quality, it does not always afford an adequate formulation as motion in real sequences is not always linear. More accurate SI have been generated in DVC by the modelling of higher-order trajectories [8][9][10][11] which includes the HOPTTI algorithm [10].…”