No-Reference Video Quality Assessment Model for Distortion Caused by Packet Loss in the Real-Time Mobile Video Services

被引:3
|
作者
Song, Jiarun [1 ]
Yang, Fuzheng [1 ]
机构
[1] Xidian Univ, Sch Telecommunicat Engn, State Key Lab ISN, Taibai Rd, Xian 710071, Peoples R China
关键词
D O I
10.1155/2014/606493
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Packet loss will make severe errors due to the corruption of related video data. For most video streams, because the predictive coding structures are employed, the transmission errors in one frame will not only cause decoding failure of itself at the receiver side, but also propagate to its subsequent frames along the motion prediction path, which will bring a significant degradation of end-to-end video quality. To quantify the effects of packet loss on video quality, a no-reference objective quality assessment model is presented in this paper. Considering the fact that the degradation of video quality significantly relies on the video content, the temporal complexity is estimated to reflect the varying characteristic of video content, using the macroblocks with different motion activities in each frame. Then, the quality of the frame affected by the reference frame loss, by error propagation, or by both of them is evaluated, respectively. Utilizing a two-level temporal pooling scheme, the video quality is finally obtained. Extensive experimental results show that the video quality estimated by the proposed method matches well with the subjective quality.
引用
下载
收藏
页数:15
相关论文
共 50 条
  • [21] Neural network solution fora real-time no-reference video quality assessment of H.264/AVC video bitstreams
    Fazliani, Yasamin
    Andrade, Ernesto
    Shirani, Shahram
    MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (02) : 2409 - 2427
  • [22] No-Reference Video Quality Assessment Using Distortion Learning and Temporal Attention
    Kossi, Koffi
    Coulombe, Stephane
    Desrosiers, Christian
    Gagnon, Ghyslain
    IEEE ACCESS, 2022, 10 : 41010 - 41022
  • [23] No-Reference Video Shakiness Quality Assessment
    Cui, Zhaoxiong
    Jiang, Tingting
    COMPUTER VISION - ACCV 2016, PT V, 2017, 10115 : 396 - 411
  • [24] Predictive no-reference assessment of video quality
    Vega, Maria Torres
    Mocanu, Decebal Constantin
    Stavrou, Stavros
    Liotta, Antonio
    SIGNAL PROCESSING-IMAGE COMMUNICATION, 2017, 52 : 20 - 32
  • [25] COME for No-Reference Video Quality Assessment
    Wang, Chunfeng
    Su, Li
    Zhang, Weigang
    IEEE 1ST CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2018), 2018, : 232 - 237
  • [26] Predictive no-reference assessment of video quality
    Torres Vega M.
    Mocanu D.C.
    Stavrou S.
    Liotta A.
    Torres Vega, Maria (m.torres.vega@tue.nl), 1600, Elsevier B.V., Netherlands (52): : 20 - 32
  • [27] An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis
    Mukesh Kumar Rohil
    Neetika Gupta
    Prakash Yadav
    Signal, Image and Video Processing, 2020, 14 : 205 - 213
  • [28] Full-reference video quality metric assisted the development of no-reference bitstream video quality metrics for real-time network monitoring
    Sedano, Inigo
    Brunnstrom, Kjell
    Kihl, Maria
    Aurelius, Andreas
    EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, 2014,
  • [29] An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis
    Rohil, Mukesh Kumar
    Gupta, Neetika
    Yadav, Prakash
    SIGNAL IMAGE AND VIDEO PROCESSING, 2020, 14 (01) : 205 - 213
  • [30] Full-reference video quality metric assisted the development of no-reference bitstream video quality metrics for real-time network monitoring
    Iñigo Sedano
    Kjell Brunnström
    Maria Kihl
    Andreas Aurelius
    EURASIP Journal on Image and Video Processing, 2014