Journal of Information Technology & Software Engineering
Open Access

A review on low-complexity video compression with compressive sensing-based approaches

Global Summit and Expo on Multimedia & Applications

August 10-11, 2015 Birmingham, UK

Li-Wei Kang

Scientific Tracks Abstracts: J Inform Tech Soft Engg

Abstract :

Wireless multimedia sensor networks (WMSNs) have been potentially applicable for several emerging applications. The
resources (e.g., power and bandwidth) available to visual sensor nodes (VSNs) in a WMSN are, however, very limited.
Therefore, it is important but challenging to achieve efficient resource allocation and optimal multimedia data compression
while maximizing the overall network lifetime. To achieve this, low-complexity and high-efficiency video encoding equipped
in each VSN is one essential issue, which is the focus in this paper. In this paper, a review and comparative study on lowcomplexity
video compression are presented. Current video compression standards (e.g., H.264/AVC or H.265/HEVC) usually
perform complex inter-frame encoding based on motion estimation with high computational complexity, which will consume a
significant portion of the total power consumption for a VSN. Recently, several “motion estimation-free” low-complexity video
encoding approaches have been proposed, which may be roughly classified into the following categories: (1) Still image-based
or standard codec-based low-complexity video compression: without performing motion estimation, the most straightforward
way is to apply still-image/intra-frame encoding to each video frame individually; (2) collaborative video compression and
transmission: To further increase compression efficiency, inter-view correlation among VSNs may be exploited via collaborative
video compression and transmission; and (3) distributed video coding (DVC): the unique characteristic is that individual
frames are encoded independently, but decoded jointly. The major encoding burden, i.e., motion estimation, can be shifted
to the decoder while preserving a certain coding efficiency. Recently, a novel compression platform based on the compressive
sensing (CS) theory has been highly popular and applicable to image and video signal acquisition and compression.
A unique characteristic is that CS can “directly” capture compressed video data without temporally storing the raw data,
resulting in very low complexity compression. Relying on the sparsity of video data, video construction can be achieved at the
decoder. In this paper, we also present an overview of our pioneer CS-based video compression framework, called distributed
compressive video sensing (DCVS) as well as the comparative study among our framework and some significantly recent
approaches inspired by our work.

Biography :

Li-Wei Kang received his BS, MS, and PhD degrees in Computer Science from National Chung Cheng University, Chiayi, Taiwan, in 1997, 1999, and 2005,
respectively. Since February 2013, he has been with the Graduate School of Engineering Science and Technology-Doctoral Program, and the Department of
Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan, as an Assistant Professor. Before that, he
worked for the Institute of Information Science, Academia Sinica (IIS/AS), Taipei, Taiwan, as an Assistant Research Scholar during 2010–2013, and as a Postdoctoral
research fellow during 2005–2010. His research interests include multimedia content analysis and multimedia communications