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The optimal coding procedure optimized according to these criteria is summarized in Appendix, where also a few implementation details are discussed.
Third, it utilizes a complexity-distortion model to determine the optimal coding parameter values to achieve global optimization.
We refer the reader to [24] for more details on the optimal coding scheme for MAC.
With the optimal coding rate known, we can select the segment with most closest coding rate.
As shown by a counterexample in [4], joint coding across multiple subchannels outperforms individually optimal coding.
Based on the estimated transmission rate, we can determine the optimal coding rate we should request.
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Lagrangian optimization is used to find the optimal code rate allocation, from a PSNR perspective, starting from commonly available source coding outputs, such as intermediate rate-distortion traces.
Inspection of the optimal codes readily reveals the main source of this non-optimality: in all optimal solutions Arg changes its position from the fourth to the third column of the table (Fig. 11b).
As evolution has no foresight, optimality of the modern genetic code suggests that it evolved from less optimal code variants.
Arithmetic coding encodes strings of symbols as ranges of real numbers and achieves more nearly optimal codes.
The two examples just considered might suggest that it is always easy to find an optimal code.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com