Content-Based Video Quality Prediction for HEVC Encoded Videos Streamed Over Packet Networks

Abstract

A new reference-free, objective, video quality prediction model that takes into account video content type to predict the quality of streamed high efficiency video coding (HEVC) encoded video sequences is proposed. Research has shown that for the same encoder settings and network quality of service (NQoS), the video quality differs for different types of video content . This indicates that, in addition to encoder settings and NQoS, there may be other key parameters that impact video quality. In this work, we hypothesized that video content type is one of the key parameters that may impact the quality of streamed videos. Based on this assertion, temporal information is extracted from the motion vector (MV) information inherent in the encoded video bitstreams and spatial information is extracted from the quantisation parameter (QP) and the number of bits (Bits) of coded intra (I) and predictive (P) frames to develop a metric that quantifies the content type of different video sequences . The content type metric is subsequently used together with encoding QP setting and network packet loss rate (PLR) to develop a reference -free objective video quality prediction model for streamed HEVC encoded video sequences. This model has an accuracy of 92% when the model predicted values of sequences not used in model derivation are compared with mean opinion score (MOS) obtained through subjective method.