  In the mobile communication field, some of the video applications boosted the
interest of robust methods for video quality assessment. Out of all existing
methods, We Preferred, No Reference Video Quality Assessment is the one which
is most needed in situations where the handiness of reference video is
partially available. Our research interest lies in formulating and melding
effective features into one model based on human visualizing characteristics.
Our work explores comparative study between Supervised and unsupervised
learning methods. Therefore, we implemented support vector regression algorithm
as NR-based Video Quality Metric(VQM) for quality estimation with simplified
input features. We concluded that our proposed model exhibited sparseness even
after dimension reduction for objective scores of SSIM quality metric.
