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Distributed coding/decoding complexity in video sensor networks.

Cordeiro PJ, Assunção P - Sensors (Basel) (2012)

Bottom Line: Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate.Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted.The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality.

View Article: PubMed Central - PubMed

Affiliation: Instituto Politécnico de Leiria/ESTG, Alto Vieiro, Leiria, Portugal. paulo.cordeiro@ipleiria.pt

ABSTRACT
Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality.

No MeSH data available.


Related in: MedlinePlus

Computational complexity distribution in a typical H.264/AVC decoding process.
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f6-sensors-12-02693: Computational complexity distribution in a typical H.264/AVC decoding process.

Mentions: High complexity in motion compensation is mainly due to the interpolation needed to decode motion vectors with half or quarter pixel accuracy. For the baseline H.264 decoder, it was shown that interpolation takes around 39% of the execution time on average, and it can go up to 44% for some sequences. Figure 6 shows the breakdown of the complexity of a typical H.264/AVC decoder implementation as reported in [25].


Distributed coding/decoding complexity in video sensor networks.

Cordeiro PJ, Assunção P - Sensors (Basel) (2012)

Computational complexity distribution in a typical H.264/AVC decoding process.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376628&req=5

f6-sensors-12-02693: Computational complexity distribution in a typical H.264/AVC decoding process.
Mentions: High complexity in motion compensation is mainly due to the interpolation needed to decode motion vectors with half or quarter pixel accuracy. For the baseline H.264 decoder, it was shown that interpolation takes around 39% of the execution time on average, and it can go up to 44% for some sequences. Figure 6 shows the breakdown of the complexity of a typical H.264/AVC decoder implementation as reported in [25].

Bottom Line: Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate.Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted.The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality.

View Article: PubMed Central - PubMed

Affiliation: Instituto Politécnico de Leiria/ESTG, Alto Vieiro, Leiria, Portugal. paulo.cordeiro@ipleiria.pt

ABSTRACT
Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality.

No MeSH data available.


Related in: MedlinePlus