Video Quality Assessment using Deep Learning

A Keras and Tensorflow implementation of video quality assessment using deep neural networks is proposed. We propose CNN + LSTM architecture to recognize and synthesize both spatial and temporal artifacts of video impairements respectively. The architecture is shown below.

Network Architecture

Training

• We used video samples of 30 seconds each to train the model.
• We first extract individual frames of the video from each video sample and create a numpy array out frames.
• We create numpy array of MOS (i.e, video quality mean opinion score collected from users) that is corresponding to each sample.
• We feed these frames of a video sample to CNNs followed by a series of Time Distributed LSTM.
• Finally, a softmax is used to classify the video sample quality.

Prerequisites

• Python3
• Tensorflow
• Keras
• Jupyter Notebook (optional)

Authors

Mallesham Dasari, Pranjal Sahu, Yang Qiu