This work addresses a significant gap in existing
image and video databases commonly used in computer vision
applications by introducing a unique and comprehensive
database named Photo-realistic Multi-Distortion Video Dataset
for Benchmarking and Developing Robust Computer Vision
Models (PVD4RCV). A key innovation of PVD4RCV lies in
its incorporation of some relevant physical factors (e.g. depth
information, interaction of light with scene contents) inherent to
video signal acquisition in constrained and complex real-world
environments, which are used to generate realistic distortions
in video sequences (e.g. local motion blur, local defocus blur).
PVD4RCV includes a diverse collection of videos featuring
common distortions, real-world scenarios, and contextual variations.
It includes both original and degraded video versions,
along with detailed annotations to support the development
of advanced learning models, particularly for tasks such as
distortion classification and object detection. This resource aims
to advance research and applications in computer vision by
providing a robust foundation for model training and evaluation.