A low-complexity psychometric curve-fitting approach for the objective quality assessment of streamed game videos

作者：

Highlights：

• A RR framework for real-time quality assessment of passive Game Video Streaming is presented.

• It uses a low-complexity psychometric curve-fitting approach to ensure scalability.

• A new objective FR metric, called GVSQM, is proposed.

• It is evaluated on the well-known Gaming VideoSET by Barman et al.

• The framework obtains a 0.903 correlation to subjective scores.

摘要

•A RR framework for real-time quality assessment of passive Game Video Streaming is presented.•It uses a low-complexity psychometric curve-fitting approach to ensure scalability.•A new objective FR metric, called GVSQM, is proposed.•It is evaluated on the well-known Gaming VideoSET by Barman et al.•The framework obtains a 0.903 correlation to subjective scores.

论文关键词：Game video streaming (GVS),Quality of Experience (QoE),Predictive modelling,Objective quality assessment,Curve-fitting,Game Video Streaming Quality Metric (GVSQM)

论文评审过程：Received 31 October 2019, Revised 11 June 2020, Accepted 16 July 2020, Available online 22 July 2020, Version of Record 1 August 2020.

论文官网地址：https://doi.org/10.1016/j.image.2020.115954