Low Complex Blind Video Quality Predictor based on Support Vector Machines

Abstract: Objective Video Quality Assessment plays a vital role in visual processing systems and especially in the mobile communication field, some of the video applications boosted the interest of robust methods for quality assessment. Out of all existing methods for Video Quality Analysis, No-Reference (NR) Video Quality Assessment is the one which is most needed in situations where the handiness of reference video is not available. Our challenge lies in formulating and melding effective features into one model based on human visualizing characteristics. Our research work explores the tradeoffs between quality prediction and complexity of a system. Therefore, we implemented support vector regression algorithm as NR-based Video Quality Metric(VQM) for quality estimation with simplified input features. The features are obtained from extraction of H.264 bitstream data at the decoder side of the network. Our metric predicted with Pearson correlation coefficient of 0.99 for SSIM, 0.98 for PEVQ, 0.96 for subjective score and 0.94 for PSNR metric. Therefore in terms of prediction accuracy, the proposed model has good correlation with all deployed metrics and the obtained results demonstrates the robustness of our approach. In our research work, the proposed metric has a good correlation with subjective scores which concludes that proposed metric can be employed for real time use, since subjective scores are considered as true or standard values of video quality.