Distributed compressed video sensing

• Published in: 2009 16th IEEE International Conference on Image Processing (ICIP) pp. 1393 - 1396
• Main Authors: Do, T.T., Yi Chen, Nguyen, D.T., Nguyen, N., Lu Gan, Tran, T.D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2009
• Subjects: Analog-digital conversion; Compressed sensing; compressive sensing; Decoding; distributed video coding; Encoding; Image reconstruction; Particle measurements; Predictive models; sparse recovery with decoder side information; structurally random matrices; Video codecs; Video coding; Video compression; Wyner-Ziv coding
• ISBN: 9781424456536; 1424456533
• ISSN: 1522-4880
• DOI: 10.1109/ICIP.2009.5414631

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 sparse recovery with side information. 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 sparse recovery with side information 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.