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The second step then tries to further simplify the affine LPV model, which helps to reduce both the computational burden for FDD synthesis methods and the order of the linear fractional representations (LFRs) that are generated from these LPV models.
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DCT algorithm to different image blocks, thus lowering both the bandwidth usage and the computational burden.
As a result, both the irrelevant information and the computational burden of a later transformation and/or classification stage are significantly decreased.
Simulation results show that the 2D SLIM compared to the 1D SLIM drastically reduces the computational burden while both of them have the same performance.
Since both equi-SNR and equi-PSNR graphs are independently prepared, the computational burden can be dramatically reduced.
The computational burden is thus significantly lower.
Section 3 analyzes the computational burden.
This further increases the computational burden.
Nevertheless, such an approach unavoidably raises again the computational burden.
Therefore, the computational burden of the basic model is heavy.
This method can significantly reduce the computational burden.
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CEO of Professional Science Editing for Scientists @ prosciediting.com