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On each segment, 1D DCT is performed and a subset of coefficients are selected.
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In work [9], the knowledge available as known zone values in the slice is translated in the coarse representation basis: a subset of Gaussian coefficients is fitted to values in the known zone (Omega); these coefficients are then used as a constraint for the reconstruction.
Each subspace in this approach keeps a subset of the coefficients and discards the rest.
B-Splines are of compact support, and therefore, the summing needs only to be carried out over a subset of all coefficients.
For each block of size p × p in the transformed domain, a subset of host coefficients with length l ≤ p2 is selected to be the carrier vector for embedding.
At each time t, the measurement process can be modelled as: begin{aligned} y_t = varPhi _t x_t + n_t, end{aligned} (3)where random variable y t which is called measurements in this application is a subset of DFT coefficients determined by the measurement matrix (varPhi _t,) and random variable n t refers to the measurement noise.
It can be observed that only a subset of regression coefficients lay below the one half threshold line.
In other words, we need to find an optimal subset of coefficients in the th DCT block to minimize the cost: (14).
Hence, is selected as a subset of quantized DCT coefficients.
These algorithms update only a subset of the filter coefficients in each time iteration.
Some efforts were made by Kuang et al. (2010) in which a subset of the Gauss coefficients (of lower degrees) with much longer records are assimilated first to spin up the model, followed by assimilating those of higher degrees for the past 100 years.
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