6,705 research outputs found
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
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
We propose a new architecture for distributed image compression from a group
of distributed data sources. The work is motivated by practical needs of
data-driven codec design, low power consumption, robustness, and data privacy.
The proposed architecture, which we refer to as Distributed Recurrent
Autoencoder for Scalable Image Compression (DRASIC), is able to train
distributed encoders and one joint decoder on correlated data sources. Its
compression capability is much better than the method of training codecs
separately. Meanwhile, the performance of our distributed system with 10
distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of
the performance of a single codec trained with all data sources. We experiment
distributed sources with different correlations and show how our data-driven
methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding
(DSC). To the best of our knowledge, this is the first data-driven DSC
framework for general distributed code design with deep learning
Power-Rate-Distortion Analysis for Wireless Video Communication under Energy Constraints
Digital Object Identifier 10.1109/TCSVT.2005.846433Mobile devices performing video coding and streaming over wireless and pervasive communication networks are limited in energy supply. To prolong the operational lifetime
of these devices, an embedded video encoding system should be able to adjust its computational complexity and energy consumption as demanded by the situation and its environment. To analyze, control, and optimize the rate-distortion (R-D) behavior of the wireless video communication system under
the energy constraint, we develop a power-rate-distortion (PR-D) analysis framework, which extends the traditional R-D analysis by including another dimension, the power consumption. Specifically, in this paper, we analyze the encoding mechanism of typical video coding systems, and develop a parametric video encoding architecture which is fully scalable in computational complexity. Using dynamic voltage scaling (DVS), an energy consumption management technology recently developed in CMOS circuits design, the complexity scalability can be translated into
the energy consumption scalability of the video encoder. We investigate the R-D behavior of the complexity control parameters and establish an analytic P-R-D model. Both theoretically and experimentally, we show that, using this P-R-D model, the video
coding system is able to automatically adjust its complexity control parameters to match the available energy supply of the mobile device while maximizing the picture quality. The P-RD
model provides a theoretical guideline for system design and performance optimization in mobile video communication under energy constraints
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