Abstract
In this paper, a Multi-phase Reconstruction (MPR) framework that uses certain key frames to produce some Side Information (SI) to improve the reconstruction quality of the non-key frames is proposed. After a sequence of frames has been encoded using Block Compressive Sensing (BCS) and transmitted to the host workstation, some SI is produced by first aligning the key frames to the non-key frames. The aligned frames are then fused together using Wavelet to exploit the spatial and temporal correlations between them, and to generate a set of predicted non-key frames. Next, the difference between the initially reconstructed and the predicted non-key frames at the measurement level is calculated. The difference is then decoded to recover a set of residual frames. The reconstruction of the final non-key frames is completed by adding the residual frames to the predicted non-key frames. The experimental results show that the proposed framework is able to outperform other frameworks by 1.5–3.0 dB at lower sub-rates.
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Ebrahim, M., Chia, W.C. (2016). Block Compressive Sensing (BCS) Based Multi-phase Reconstruction (MPR) Framework for Video. In: Soh, P., Woo, W., Sulaiman, H., Othman, M., Saat, M. (eds) Advances in Machine Learning and Signal Processing. Lecture Notes in Electrical Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-319-32213-1_10
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DOI: https://doi.org/10.1007/978-3-319-32213-1_10
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