NVL2E - Neural Network-Based Video Feature Extraction Enabled Loss Computation to Learn and Evaluate Video Quality

Abstract

There are many more needs and demands in standardizing video quality. Different models of the video collection depend on exclusive features for a particular database, because of the complicated relations among these features; we require various modes of unified symbol database representation. Sometimes the right kind of database may not be available, or it may contain very small packages. As well as the correct calculation methods that can formalize it have not been implemented. It is difficult to separate video and its quality if it has very little data and features. The steps required to detect and eliminate these defects have been identified in this proposed design using the deep learning calculation method in machine learning. This method NVL2E – Neural Network-based Video feature extraction Enabled subsequently loss computation done to learn and Evaluate Video quality deals with the calculations required to extract common features, and the extraction of its special features by the neural network. Next, these two features are combined and presented to the neural calculation for further quality checking. Later, the learned features by using neural network will be extracted to define the video quality. This allows us to see the best video quality results.