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Laser & Optoelectronics Progress, Volume. 57, Issue 18, 181509(2020)

No Reference Video Quality Assessment Based on Spatio-Temporal Features and Attention Mechanism

Ze Zhu1, Qingbing Sang1,2、*, and Hao Zhang1
Author Affiliations
  • 1School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • 2Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Wuxi, Jiangsu 214122, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (17)
    Equations (0)
    References (25)
    Cited By (1)
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    Paper Information
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    Figures & Tables(17)
    Network structure

    Fig. 1. Network structure

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    Schematic of GRU network structure

    Fig. 2. Schematic of GRU network structure

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    Attention model

    Fig. 3. Attention model

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    1st frame of different distorted videos. (a) Riverbed; (b) sunflower; (c) station; (d) tractor

    Fig. 4. 1st frame of different distorted videos. (a) Riverbed; (b) sunflower; (c) station; (d) tractor

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    Flow chart of video data processing

    Fig. 5. Flow chart of video data processing

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    Scatter plot of prediction results on LIVE video library

    Fig. 6. Scatter plot of prediction results on LIVE video library

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    Relationship curves between number of training sets of different proportions and evaluation results

    Fig. 7. Relationship curves between number of training sets of different proportions and evaluation results

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    Scatter plot of prediction results on CSIQ video library

    Fig. 8. Scatter plot of prediction results on CSIQ video library

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    Scatter plot of prediction results on IVP video library

    Fig. 9. Scatter plot of prediction results on IVP video library

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    • Table 1. Network parameter setting

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      Table 1. Network parameter setting

      Layer nameOutput sizeParameter
      Conv1,Conv224000×48×64Size: 3×3; filters: 64
      Max pooling112000×24×64Size: 2×2; stride: 2×2
      Conv3,Conv412000×24×128Size: 3×3; filters: 128
      Max pooling26000×12×128Size: 2×2; stride: 2×2
      Conv5,Conv6,Conv76000×12×256Size: 3×3; filters: 256
      Max pooling33000×6×256Size: 2×2; stride: 2×2
      Conv8,Conv9,Conv103000×6×512Size: 3×3; filters: 512
      Max pooling41500×3×512Size: 2×2; stride: 2×2
      Conv11,Conv12,Conv131500×3×512Size: 3×3; filters: 512
      Max pooling5749×1×512Size: 2×2; stride: 3×3
      GRU1×1×512512
      Attention1×512/
      FC1×11

    • Table 2. Performance comparison of different algorithms on LIVE video library

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      Table 2. Performance comparison of different algorithms on LIVE video library

      AlgorithmSROCCPLCC
      PSNR[23]0.53980.5645
      SSIM[24]0.73640.7470
      ST-MAD[6]0.82510.8332
      STRRED[25]0.80070.8119
      FS-MOVIE[7]0.84820.8636
      V-BLIINDS[4]0.83770.8471
      Ours without attention0.85570.8633
      Ours with attention0.87980.8910

    • Table 3. Comparison of SROCC values of different algorithms for single distortion type

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      Table 3. Comparison of SROCC values of different algorithms for single distortion type

      AlgorithmWirelessIPH.264MPEG-2
      PSNR[23]0.65740.41670.45850.3862
      SSIM[24]0.72890.65340.73130.6684
      ST-MAD[6]0.80990.77580.90210.8461
      STRRED[25]0.78570.77220.81930.7193
      FS-MOVIE[7]0.81390.77220.84900.8609
      V-BLIINDS[4]0.84550.78980.85870.8377
      Ours withoutattention0.84870.83160.84680.8331
      Ours withattention0.86170.84580.85850.8547

    • Table 4. Comparison of PLCC values of different algorithms for single distortion type

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      Table 4. Comparison of PLCC values of different algorithms for single distortion type

      AlgorithmWirelessIPH.264MPEG-2
      PSNR[23]0.70580.47670.57460.3986
      SSIM[24]0.71840.77640.74200.6222
      ST-MAD[6]0.85910.80650.87960.8560
      STRRED[25]0.80530.85270.81410.7570
      FS-MOVIE[7]0.85990.80090.87650.8721
      V-BLIINDS[4]0.91340.90200.90380.8699
      Ours withoutattention0.90690.90990.87660.8745
      Ours withattention0.92030.91770.89620.8858

    • Table 5. Comparison of final evaluation results on LIVE video library

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      Table 5. Comparison of final evaluation results on LIVE video library

      AlgorithmLiveData1LiveData2LiveData3LiveData4Average
      SROCCPLCCSROCCPLCCSROCCPLCCSROCCPLCCSROCCPLCC
      Ours withoutattention0.84780.87520.84820.86720.85440.84610.87240.86480.85570.8633
      Ours with attention0.86930.89080.89100.90040.88520.87580.87350.89690.87980.8910

    • Table 6. Comparison of running time of different methods on “Tractor” video

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      Table 6. Comparison of running time of different methods on “Tractor” video

      AlgorithmTime /s
      PSNR[23]3.09
      SSIM[24]11.34
      ST-MAD[6]335.90
      STRRED[25]54.94
      FS-MOVIE[7]4444.20
      Ours with attention1291.20

    • Table 7. Performance comparison of different algorithms on CSIQ video library

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      Table 7. Performance comparison of different algorithms on CSIQ video library

      AlgorithmSROCCPLCC
      PSNR[23]0.72530.7932
      SSIM[24]0.86610.8517
      ST-MAD[6]0.81740.8266
      STRRED[25]0.88220.8734
      FS-MOVIE[7]0.80670.8053
      V-BLIINDS[4]0.83510.8449
      Ours with attention0.89090.8991

    • Table 8. Performance comparison of different algorithms on IVP video library

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      Table 8. Performance comparison of different algorithms on IVP video library

      AlgorithmSROCCPLCC
      PSNR[23]0.70640.7299
      SSIM[24]0.76940.7667
      ST-MAD[6]0.82350.8284
      STRRED[25]0.87610.8853
      FS-MOVIE[7]0.81770.8359
      V-BLIINDS[4]0.85520.8441
      Ours with attention0.90640.9135
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    Ze Zhu, Qingbing Sang, Hao Zhang. No Reference Video Quality Assessment Based on Spatio-Temporal Features and Attention Mechanism[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181509

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    Paper Information

    Category: Machine Vision

    Received: Jan. 7, 2020

    Accepted: Feb. 24, 2020

    Published Online: Sep. 2, 2020

    The Author Email: Sang Qingbing (sangqb@163.com)

    DOI:10.3788/LOP57.181509

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology