Automatic and efficient quality analysis of audiovisual content has become a crucial step before storing the material for later use. While most approaches in this area are only dealing with low level signal analysis, the goal of this project is to go far beyond state-of-the-art procedures. On the basis of novel as well as proven computer vision methods, we will attempt to incorporate high level knowledge in the analysis step, thus achieving significant better and faster results than current methods, comparable in their reliability with a human operator.
In particular the vdQA project will carry out research in the following areas:
Improvement of optical flow field methodologies to deal with multi-frame information
Application of novel segmentation methods in order to enable semantic quality analysis.
Knowledge assisted artefact assessment and classification.
Novel methods for fast and robust detection of difficult impairments like unsteadiness, flicker, freeze frames, test patterns and lost frames.
Research into methodologies that are particularly well suited for implementations taking advantage of GPU hardware.
The grand challenge in the end is the combination of robustness, speed and integration of human knowledge. The research and industrial partners have dedicated roles in the work programme to achieve those goals. The industrial partners have excellent knowledge of the market and will provide user requirements as well as extensive test material. The academic partners will do research in their respective fields, namely development of basic algorithms for optical flow, tracking, segmentation, classification and usage of GPUs as well as algorithms for content based quality analysis and semantic technologies to represent knowledge. Towards the project end the industrial partners will evaluate and test the developments together with pilot end users.