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.

翻译：在移动通信领域,一些视频应用提高了对稳健的视频质量评估方法的兴趣。在所有现有方法中,我们首选“不参考视频质量评估”是参考视频易用性部分可用情况下最需要的方法。我们的研究兴趣在于根据人的可视化特征将有效特征制成和焊接成一个模型。我们的工作探索了受监督和不受监督的学习方法之间的比较研究。因此,我们用基于NR的视频质量Metri(VQM)支持矢量回归算法,作为基于NR的视频质量Metri(VQM)的质量评估,并附有简化的输入特征。我们的结论是,我们提议的模型在降低SSIM质量指标的客观分数之后仍然显得稀少。</s>