No-Reference Deep Compressed-based Video Quality Assessment

被引：0
|
作者：
Alizadeh, M. [1 ]
Mohammadi, A. [1 ]
Sharifkhani, M. [1 ]
机构：
[1] Sharif Univ Technol, Dept Elect Engn, Tehran, Iran
来源：
2018 8TH INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE) | 2018年
关键词：
Compressed Domain; Video quality assessment; HEVC;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel No-Reference Video Quality Assessment (NR-VQA), based on Convolutional Neural Network (CNN) for High Efficiency Video Codec (HEVC) is presented. Deep Compressed-domain Video Quality (DCVQ) measures the video quality, with compressed domain features such as motion vector, bit allocation, partitioning and quantization parameter. For the training of the network, P-MOS is used due to the limitation of existing datasets. The evaluation of the proposed method shows that it has "96%" correlation to subjective quality assessment (MOS). The method can work simultaneously with the decoding process and measures the quality in different resolutions.
引用
收藏
页码：130 / 134
页数：5
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