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Since one may pick and choose from multiple possible metrics to add post hoc, the impact of the added metric achieving statistical significance is lessened.
NNMF performed better on this metric, achieving a correlation coefficient of 0.65.
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Therefore, an important part of this study is the definition of a link quality metric, achieved by using local polynomial Kernel Estimators.
With the evaluation on public LIVE phase-I, LIVE phase-II, and IVC stereoscopic image databases, the proposed no-reference metric achieves the state-of-the-art performance for quality assessment of stereoscopic images, and is even competitive to existing full-reference quality metrics.
The RlogF metric achieved 61% recall, 72% precision, with an F-score of 0.66.
The KLD metric achieved 61% recall, 70% precision, with an F-score of 0.65.
SSIM-based VSQA metric achieves the gain of 17.8 % over SSIM in correlation with subjective measurements.
The proposed no-reference metric achieves consistently a good performance across noise types and across databases as compared to many of the best very recent no-reference quality metrics.
A drawback of this second option is that, though the quality of each link of a path is taken into account, the round function introduces quantification error, which may preclude the metric from achieving the best performance.
Thus, we normalize this metric to achieve a range of 0[1].
By using this metric we achieve a more stable route selection process and improved throughput in comparison to the OLSR-based approach currently used in Community Networks.
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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