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As we have mentioned earlier, the proposed pDBM is incorporated into the encoder and decoder in the same way to generate McFIS from the decoded frames so that we do not need to encode an McFIS.
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Again, we measure the corresponding extrinsic MI and the decoder trajectory in the same manner as we measure above.
The results given in Table 4 show that the computational load of the encoders is similar, but they differ greatly in the decoders load: Convolutional decoder requires the same time as the LDPC decoder with 10 iterations.
As for the hemispheric decoder, patterns were computed in the same frequency band in which the decoder is tested.
In the decoder, the same prediction operations are performed on Y w to get E w.
Although the LMMSE decoder has the same structure for information embedding in the DCT and magnitude of the DFT, its performance varies in these host domains.
The pattern match decoder shows the same trend, but the error remains constant on a larger range.
However, the error still remains higher than in the simple case when the decoder is trained and tested in the same frequency band.
It has been shown in [40] that LC-K-Best decoder achieves almost the same performance as the classical K-Best decoder with different modulations.
Since the two descriptions are generated by odd and even splitting, at the central decoder the video streams from the standard decoder can be interleaved and realigned in the same way firstly.
The computational complexity of such a decoder is approximately the same as that of an HE-AAC decoder operating in normal stereo mode, where AAC decoding, QMF analysis filtering, and SBR processing have to be carried out for both channels of a stereo signal [55, 57]. Figure 24 Decoder structure of HE-AAC v2.
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