Performance Analysis Of No-reference Video Quality Assessment Methods For Frame Freeze and Frame Drop Detection

Data transmission through a network has become a challenging task considering the loss of data factor. This is similar in the case of video transmission through a network. Error prone channels present in the network are responsible for the quality degradation of decoded video. There must be some metric monitoring the quality of this decoded video. Lot of research work is done in this field and many metrics are designed for video quality estimation considering various factors. Most of the metrics are designed considering only few factors thereby limiting their scope for few videos. In this research we investigate few metrics and propose the best metric by implementing them on different videos. Best metric proposed gives the video quality estimation independent of resolution and artifact in a video. Subjective analysis is conducted for validation of our results. Metrics that are implemented belong to NO-REFERENCE video quality metric group