Abstract
Distributed video coding (DVC) is an attractive and promising scheme that suits the constrained video applications, such as wireless sensor networks or wireless surveillance systems. In DVC, estimation of fast and consistent side information (Տį) is a critical issue for instant and real-time decoding. This issue becomes even more serious for high-resolution videos. Therefore, to minimise the side information estimation computational complexity, in this work, a computationally low complex DVC codec is proposed, which uses a simple phase interpolation (Phase-I) algorithm. It performs faster for all resolutions videos, and significant results are achieved for high-resolution videos with a large group of pictures (GOP). For the proposed technique, the computation time rapidly decreases with an increase in resolution. It performs 221% to 280% faster from conventional frame interpolation method for high-resolution videos and large GOP at the cost of little degradation in the visual quality of estimated side information.
Highlights
Wireless video sensor networks (WVSNs) are capable of capturing video at distributed video sensor nodes
Afterwards, the codec known as PRISM (Power-efficient, Robust, hIgh compression Syndrome based Multimedia coding) for transform domain (TD) was proposed by Puri et al [26, 27]
The Phase interpolation (Phase-I) is much faster than motion-compensated temporal interpolation (MCTI) for all resolution videos and delivers optimum performance for different group of pictures (GOP) sizes
Summary
Wireless video sensor networks (WVSNs) are capable of capturing video at distributed video sensor nodes. Due to challenges of being battery supported, there is a need for efficient use of storage resources and lower energy consumption in WVSNs [6]. Such video sensors, demand low encoding techniques to compress the video to the lower bit rate before storing or transmitting it [7] and to reduce the transmission delay [8]. One of the supportive coding approaches is distributed video coding (DVC) that redistributes the coding complexity in such a way that there is much low encoding computation [9] while decoding can be more complex [10]. The traditional Տ į generation algorithms are computationally extensive due to the complex nature of the prediction process and take a lot of time even for a lowresolution video
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