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Volume: 33 | Article ID: art00024
Exploring the boundaries of an AE-based quality model: a performance analysis via synthetic content
  DOI :  10.2352/ISSN.2470-1173.2021.9.IQSP-266  Published OnlineJanuary 2021

The No-reference Autoencoder VidEo (NAVE) metric is a video quality assessment model based on an autoencoder machine learning technique. The model uses an autoencoder to produce a set of features with a lower dimension and a higher descriptive capacity. NAVE has been shown to produce accurate quality predictions when tested with two video databases. As it is a common issue when dealing with models that rely on a nested non-linear structure, it is not clear at what level the content and the actual distortions are affecting the model’s predictions. In this paper, we analyze the NAVE model and test its capacity to distinguish quality monotonically for three isolated visual distortions: blocking artifacts, Gaussian blur, and white noise. With this goal, we create a dataset consisting of a set of short-length video sequences containing these distortions for ten very pronounced distortion levels. Then, we performed a subjective experiment to gather subjective quality scores for the degraded video sequences and tested the NAVE pre-trained model using these samples. Finally, we analyzed NAVE quality predictions for the set of distortions at different degradation levels with the goal of discovering the boundaries on which the model can perform.

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Helard Becerra Martinez, André H. M. da Costa, Bruna Azambuja, Andrew Hines, Mylène C.Q. Farias, "Exploring the boundaries of an AE-based quality model: a performance analysis via synthetic contentin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XVIII,  2021,  pp 266-1 - 266-8,

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