  With the advancement of IPTV and HDTV technology, previous subtle errors in
videos are now becoming more prominent because of the structure oriented and
compression based artifacts. In this paper, we focus towards the development of
a real-time video quality check system. Light weighted edge gradient magnitude
information is incorporated to acquire the statistical information and the
distorted frames are then estimated based on the characteristics of their
surrounding frames. Then we apply the prominent texture patterns to classify
them in different block errors and analyze them not only in video error
detection application but also in error concealment, restoration and retrieval.
Finally, evaluating the performance through experiments on prominent datasets
and broadcasted videos show that the proposed algorithm is very much efficient
to detect errors for video broadcast and surveillance applications in terms of
computation time and analysis of distorted frames.
