This paper proposes a novel framework called Distributed Compressed Video Sensing (DISCOS) - a solution for Distributed Video Coding (DVC) based on the Compressed Sensing (CS) theory. The DISCOS framework compressivel...
详细信息
ISBN:
(纸本)9781424427338
This paper proposes a novel framework called Distributed Compressed Video Sensing (DISCOS) - a solution for Distributed Video Coding (DVC) based on the Compressed Sensing (CS) theory. The DISCOS framework compressively samples each video frame independently at the encoder and recovers video frames jointly at the decoder by exploiting an interframe sparsity model and by performing sparserecovery with sideinformation. Simulation results show that DISCOS significantly outperforms the baseline CS-based scheme of intraframe-coding and intraframe-decoding. Moreover, our DISCOS framework can perform most encoding operations in the analog domain with very low-complexity. This makes DISCOS a promising candidate for real-time, practical applications where the analog to digital conversion is expensive, e.g., in Terahertz imaging.
This paper proposes a novel framework called Distributed Compressed Video Sensing (DISCOS) a solution for Distributed Video Coding (DVC) based on the recently emerging Compressed Sensing theory. The DISCOS framework c...
详细信息
ISBN:
(纸本)9781424456536
This paper proposes a novel framework called Distributed Compressed Video Sensing (DISCOS) a solution for Distributed Video Coding (DVC) based on the recently emerging Compressed Sensing theory. The DISCOS framework compressively samples each video frame independently at the encoder. However, it recovers video frames jointly at the decoder by exploiting an interframe sparsity model and by performing sparserecovery with sideinformation. In particular, along with global frame-based measurements, the DISCOS encoder also acquires local block-based measurements for block prediction at the decoder. Our interframe sparsity model mimics state-of-the-art video codecs: the sparsest representation of a block is a linear combination of a few temporal neighboring blocks that are in previously reconstructed frames or in nearby key frames. This model enables a block to be optimally predicted from its local measurements by l(1)-minimization. The DISCOS decoder also employs a sparserecovery with sideinformation to jointly reconstruct a frame from its global measurements and its local block-based prediction. Simulation results show that the proposed framework outperforms the baseline compressed sensing-based scheme of intraframe-coding and intraframe-decoding by 8 - 10dB. Finally, unlike conventional DVC schemes, our DISCOS framework can perform most encoding operations in the analog domain with very low-complexity, making it be a promising candidate for real-time, practical applications where the analog to digital conversion is expensive, e.g., in Terahertz imaging.
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