• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Space-Time Quantization and Motion-Aligned Reconstruction for Block-Based Compressive Video Sensing


Abstract

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
R. Li, H. Liu, W. He and X. Ma, "Space-Time Quantization and Motion-Aligned Reconstruction for Block-Based Compressive Video Sensing," KSII Transactions on Internet and Information Systems, vol. 10, no. 1, pp. 321-340, 2016. DOI: 10.3837/tiis.2016.01.019.

[ACM Style]
Ran Li, Hongbing Liu, Wei He, and Xingpo Ma. 2016. Space-Time Quantization and Motion-Aligned Reconstruction for Block-Based Compressive Video Sensing. KSII Transactions on Internet and Information Systems, 10, 1, (2016), 321-340. DOI: 10.3837/tiis.2016.01.019.