Content aware video quality prediction model for HEVC encoded bitstream

2017 ◽  
Vol 76 (18) ◽  
pp. 19191-19209
Author(s):  
Yongfang Wang ◽  
Kanghua Zhu ◽  
Jian Wu ◽  
Yun Zhu
Keyword(s):  
Prediction Model ◽  
Video Quality ◽  
Quality Prediction ◽  
Content Aware

scholarly journals A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Debajyoti Pal ◽  
Vajirasak Vanijja
Keyword(s):  
Prediction Model ◽  
Video Transmission ◽  
Video Quality ◽  
Decision Variable ◽  
Quality Prediction ◽  
Application Layer ◽  
Neural Network Approach ◽  
Integrated Network ◽  
Final Model ◽  
Artificial Neural Network Approach

We propose a modular no-reference video quality prediction model for videos that are encoded with H.265/HEVC and VP9 codecs and viewed on mobile devices. The impairments which can affect video transmission are classified into two broad types depending upon which layer of the TCP/IP model they originated from. Impairments from the network layer are called the network QoS factors, while those from the application layer are called the application/payload QoS factors. Initially we treat the network and application QoS factors separately and find out the 1 : 1 relationship between the respective QoS factors and the corresponding perceived video quality or QoE. The mapping from the QoS to the QoE domain is based upon a decision variable that gives an optimal performance. Next, across each group we choose multiple QoS factors and find out the QoE for such multifactor impaired videos by using an additive, multiplicative, and regressive approach. We refer to these as the integrated network and application QoE, respectively. At the end, we use a multiple regression approach to combine the network and application QoE for building the final model. We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.

scholarly journals Perceptual Video Quality Prediction Emphasizing Chroma Distortions

2021 ◽  
Vol 30 ◽  
pp. 1408-1422
Author(s):  
Li-Heng Chen ◽  
Christos G. Bampis ◽  
Zhi Li ◽  
Joel Sole ◽  
Alan C. Bovik
Keyword(s):  
Video Quality ◽  
Quality Prediction ◽  
Perceptual Video Quality
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