By applying various Video Prediction models on videos from various datasets, we generate a large
collection of
predicted videos. We select a representative set of 300 videos from these to form the Indian Institute
of Science - Predicted Video Quality Assessment (IISc PVQA) Database. In the following videos, we show example
distortions observed in our Database. These videos correspond to the videos shown in the paper.
For a given video, we compute deep features of pretrained networks such as VGG-19, ResNet-50 and
Inception-v3. We further process them to get 2 sets of features. We use a shallow feed forward neural
network to learn quality score from the computed features (Figure A).
Motion-compensated Cosine Similarity (MCS) features: We compute cosine similarity between deep
features of last context frame and motion-compensated deep features of predicted frames (Figure B).
Rescaled Frame Difference (RFD) features: We compute difference of successive frames and rescale the
diff frames in the range [0,255]. We then compute deep features of them and average spatially
(Figure C).
Nagabhushan Somraj, Manoj Surya Kashi, S P Arun, Rajiv Soundararajan, "Understanding the Perceived Quality of Video Predictions", Signal Processing: Image Communication, p.116626, 2022.
Bibtex:
@article{somraj2020pvqa,
title = {Understanding the Perceived Quality of Video Predictions},
author = {Somraj, Nagabhushan and Kashi, Manoj Surya and Arun, S. P. and Soundararajan, Rajiv},
journal = {Signal Processing: Image Communication},
volume = {102},
pages = {116626},
issn = {0923-5965},
year = {2022},
doi = {10.1016/j.image.2021.116626}
}
