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No-reference video quality assessment via pretrained CNN and LSTM networks | SpringerLink
Source: link.springer.com
Article summary:
1. This paper introduces a general-purpose no-reference video quality assessment algorithm based on a long short-term memory (LSTM) network and a pretrained convolutional neural network (CNN).
2. The proposed method was trained on the Konstanz Natural Video Quality Database (KoNViD-1k), which is the only publicly available database that contains sequences with authentic distortions.
3. Results of experiments demonstrate that the proposed method outperforms other state-of-the-art algorithms, and these results are also confirmed using tests on the LIVE Video Quality Assessment Database.
Article analysis:
The article is generally reliable and trustworthy, as it provides evidence for its claims in the form of experiments conducted on two databases - KoNViD-1k and LIVE Video Quality Assessment Database - to demonstrate that the proposed method outperforms other state-of-the-art algorithms. Furthermore, it cites relevant sources to support its claims, such as ITU P913 for subjective video quality assessment, natural scene statistics (NSS) for feature extraction, CORNIA NR image quality assessment method, and so on.
However, there are some potential biases in the article that should be noted. For example, while it mentions various existing methods such as distortion-specific NRVQA algorithms and general purpose NRVQA methods, it does not provide an in depth comparison between them or explore their advantages/disadvantages over each other. Additionally, while it mentions various deep learning techniques used in NRVQA methods such as CNNs and LSTMs, it does not provide any insights into how they work or why they are better than traditional machine learning techniques.
In conclusion, this article is generally reliable and trustworthy but could benefit from more detailed comparisons between existing methods and deeper insights into deep learning techniques used in NRVQA methods.