Automatic defect detection in video archives - Application to Montreux Jazz Festival Digital Archives

Archival of audio-visual databases has become an important discipline in multimedia. Various defects are typically present in such archives. Among those, one can mention recording related defects such as interference between audio and video signals, optical related artifacts, recording and play out artifacts such as horizontal lines, and dropouts, as well as those due to digitization such as diagonal lines. An automatic or semi-automatic detection to identify such defects is useful, especially for large databases. In this paper, we propose two automatic algorithms for detection of horizontal and diagonal lines, as well as dropouts that are among the most typical artifacts encountered. We then evaluate the performance of these algorithms by making use of ground truth scores obtained by human subjects.