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SCIENCE CHINA Information Sciences, Volume 63, Issue 11: 211301(2020) https://doi.org/10.1007/s11432-019-2757-1

Perceptual image quality assessment: a survey

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  • ReceivedAug 3, 2019
  • AcceptedJan 13, 2020
  • PublishedApr 26, 2020

Abstract

Perceptual quality assessmentplays a vital role in the visual communication systems owing to theexistence of quality degradations introduced in various stages of visual signalacquisition, compression, transmission and display.Quality assessment for visual signals can be performed subjectively andobjectively, and objective quality assessment is usually preferred owing to itshigh efficiency and easy deployment. A large number of subjective andobjective visual quality assessment studies have been conducted during recent years.In this survey, we give an up-to-date and comprehensivereview of these studies.Specifically, the frequently used subjective image quality assessment databases are firstreviewed, as they serve as the validation set for the objective measures.Second, the objective image quality assessment measures are classified and reviewed according to the applications and the methodologies utilized in the quality measures.Third, the performances of the state-of-the-artquality measures for visual signals are compared with an introduction of theevaluation protocols.This survey provides a general overview of classical algorithms andrecent progresses in the field of perceptual image quality assessment.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61901260, 61831015, 61521062, 61527804).


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  • Figure 1

    (Color online) Scope of this paper.

  • Figure 2

    (Color online) An illustration of various general distortion types. All distortions included in the TID2013 database [15]are shown in this figure.

  • Figure 3

    General framework of FR IQA algorithms. Features are extracted from both images, and then the feature distance is calculated.

  • Figure 4

    Framework of the FR SSIM index [60], which measures the image luminance, contrast and structure similarity.

  • Figure 5

    General framework of RR IQA algorithms. RR features of the reference and distorted images are extracted at the sender and receiver side, respectively. Then the RR features of the reference and distorted images are used collectively to compute the quality.

  • Figure 6

    General framework of no-reference image quality assessment algorithms.

  • Figure 7

    Framework of the NR BLIINDS-II index [176], which is based on the NSS in the DCT domain.

  • Figure 8

    Framework of the NR CORNIA method [190], which is based on unsupervised feature learning.

  • Figure 9

    A general framework of free energy modeling [190].

  • Figure 10

    (Color online) An illustration of 3D distortions, including both left and right views. (a) Symmetrical; protectłinebreak (b) asymmetrical. Images are from the Waterloo 3D phase II database [24].

  • Figure 11

    (Color online) An illustration of saliency weighted IQA. (a) Reference image; (b) distorted image; (c) saliency; (d) saliency-weighted image; (e) SSIM; (f) saliency-weighted SSIM.

  • Figure 12

    (Color online) A comparison of reference and distorted NSIs and SCIs. (a) Reference NSI; (b) distorted NSI; (c) reference SCI; (d) distorted SCI. Images are from the CCT database [38].

  • Figure 13

    (Color online) Tone-mapped images derived from different TMOs. Images are from the TMID [42].

  • Figure 14

    (Color online) Images with multiple exposure levels (top) and the fused images (bottom). Images are from the MEF database [44].

  • Figure 15

    (Color online) Original (the 1st column) and retargeted images (the 2nd–4th columns). Images are from the MIT RetargetMe [31].

  • Figure 16

    (Color online) Images degraded by multiple distortions. Images are from the MDID2013 [34].

  • Figure 17

    (Color online) A comparison of simulated and authentic distortions. Images are from the LIVE [13]and LIVE Wild [41]databases.

  • Figure 18

    (Color online) DIBR synthesized images. Images are from the IRCCyN/IVC DIBR image database.

  • Figure 19

    (Color online) Hazy and dehazed images. Images are from the DHQ database.

  • Figure 20

    (Color online) Example VR images from the OIQA database.

  •   
  • Table 1   Performance of full-reference image quality assessment algorithms
    Types Metrics CSIQLIVETID2008TID2013
    SRCC PLCC SRCC PLCC SRCC PLCC SRCC PLCC
    PSNR 0.8058 0.7512 0.8756 0.8723 0.5531 0.5223
    SSIM [60] 0.8756 0.8612 0.9479 0.9449 0.7749 0.7732
    VPSNR [451] 0.96 0.98 0.92 0.96
    IW-SSIM [62] 0.9213 0.9144 0.9567 0.9522 0.8559 0.8579
    MS-SSIM [61] 0.9133 0.8991 0.9513 0.9489 0.8542 0.8451
    Spatial method NSER [71] 0.9337 0.9473 0.9419 0.9395 0.7404 0.7957
    GS [67] 0.8005 0.7998 0.8756 0.8723 0.5794 0.5726
    VGS [69] 0.9662 0.9692 0.9696 0.9686 0.8983 0.9079
    IGM [64] 0.9401 0.9280 0.9580 0.9578 0.8902 0.8858
    GMSD [68] 0.957 0.954 0.960 0.960 0.891 0.879
    SVCM [70] 0.951 0.949 0.964 0.962 0.874 0.889 0.787 0.857
    IFS [452] 0.9581 0.9576 0.9599 0.9586 0.8903 0.8810 0.8697 0.8791
    Transformation-based VSNR [19] 0.8109 0.7355 0.9271 0.9229 0.7046 0.6820
    VIF [96] 0.9195 0.9277 0.9632 0.9598 0.7496 0.8090
    MIQE [97] 0.911 0.916 0.964 0.962 0.807 0.840
    MIQEC [97] 0.930 0.926 0.961 0.960 0.788 0.829
    DCT-QM [100] 0.9332 0.7674 0.9557 0.8260 0.8392 0.6641 0.8544 0.6791
    SC-DM [101] 0.9423 0.7863 0.9475 0.8092 0.9021 0.7252 0.9003 0.7270
    SC-QI [101] 0.9434 0.7870 0.9480 0.8098 0.9051 0.7294 0.9052 0.7327
    IQDM [99] 0.9058 0.8976 0.9336 0.9536 0.8415 0.8369
    Learning-based Q [76] 0.881 0.900 0.925 0.924 0.817 0.816
    SFF [81] 0.9627 0.9643 0.9649 0.9632 0.8767 0.8817
    ParaBoost [78] 0.9733 0.9766 0.9819 0.9802 0.9772 0.9767 0.9575 0.9567
    QASD [82] 0.9516 0.9466 0.9646 0.9602 0.8899 0.8877 0.8657 0.8894
    KRR [83] 0.9141 0.9197 0.9574 0.9587 0.8865 0.8903 0.7969 0.8220
    NMF [453] 0.9727 0.9763 0.9760 0.9758 0.9466 0.9513
    DeepSim [91] 0.919 0.919 0.974 0.968 0.846 0.872
    DADF [80] 0.930 0.782
    Other FSIM [65] 0.9242 0.9120 0.9634 0.9597 0.8805 0.8738
    Li et al. [98] 0.933 0.928 0.946 0.936 0.861 0.869
  • Table 2   Performance of reduced-reference image quality assessment algorithms
    Metrics CSIQLIVETID2008TID2013
    SRCC PLCC SRCC PLCC SRCC PLCC SRCC PLCC
    RRED [111] 0.9429 0.9385
    RR-SSIM [112] 0.8527 0.8426 0.9129 0.9194 0.7210 0.7231
    Wu et al. [103] 0.732 0.725 0.528
    WNISM [113] 0.880 0.883
    REDLOG [114] 0.8576 0.8560 0.9455 0.9372 0.6864 0.7326 0.6829 0.7400
    DNT marginal [115] 0.9287 0.9173
    SPCRM-SCHARR [454] 0.8889 0.8906 0.9131 0.9097 0.7567 0.7403
  • Table 3   Performance of no-reference image quality assessment algorithms
    Types Metrics CSIQLIVETID2008TID2013
    SRCC PLCC SRCC PLCC SRCC PLCC SRCC PLCC
    BIQI [169] 0.8195 0.8205
    DIIVINE [170] 0.916 0.917
    BLIINDS-II [176] 0.9202 0.9232
    BRISQUE [177] 0.9395 0.9424
    NIQE [178] 0.9135 0.9147
    NSS-based IL-NIQE [179] 0.822 0.865 0.902 0.906 0.521 0.648
    GM-LOG [180] 0.9243 0.9457 0.9511 0.9551 0.9369 0.9406
    IDEAL [181] 0.8683 0.8913 0.9409 0.9462 0.7190 0.7674
    DESIQUE [187] 0.928 0.942 0.9437 0.9465 0.919 0.925
    C-DIIVINE [173] 0.910 0.935 0.9444 0.9474 0.921 0.925
    GLBP [184] 0.921
    LPSI [183] 0.9511 0.9542 0.9399
    CORNIA [190] 0.942 0.935 0.813 0.837
    HOSA [194] 0.9298 0.9480 0.9504 0.9527 0.9521 0.9592
    QAC [192] 0.8627 0.8768 0.8857 0.8608 0.8697 0.8377
    SRNSS [200] 0.9304 0.9318
    SOM [195] 0.964 0.962
    TCLT [202] 0.891 0.934 0.935 0.872
    Learning-basedLQP [198] 0.9109 0.9255 0.9289 0.9307 0.9244 0.9325
    BNB [205] 0.9508 0.9497
    NRHC [79] 0.8776 0.8714
    PIPs [206] 0.843 0.938 0.779
    DIPs [207] 0.930 0.949 0.958 0.957 0.877 0.894
    MRLIQ [209] 0.9219 0.9528
    SESANIA [217] 0.9340 0.9476 0.8936 0.9069
    BIECON [227] 0.961 0.962 0.923
    NFERM [238] 0.9142 0.9405 0.9457 0.9156
    HVS-based NRSL [239] 0.930 0.954 0.952 0.956 0.945 0.959
    BSD [240] 0.9330 0.9489 0.9618 0.9653 0.9557 0.9673
    DIQES [244] 0.8561 0.8879 0.8966 0.9034 0.8223 0.8103 0.8355 0.8348
  • Table 4   Performance of 3D image quality assessment algorithms
    TypesMetrics NBU 3DLIVE 3D Phase-ILIVE 3D Phase-IIMCL-3D
    SRCC PLCC SRCC PLCC SRCC PLCC SRCC PLCC
    FR Liu et al. [276] 0.9206 0.9330 0.9336 0.9459 0.9030 0.9162
    FR SDM-GSSIM [268] 0.9248 0.9332
    Binocular visual pathways FR Lin et al. [269] 0.9256 0.9391 0.9196 0.9292
    FR Lin et al. [272] 0.9314 0.9366 0.8824 0.8984
    Cyclopean images FR NN-MS-SSIM [276] 0.9385 0.9371
    NR Zhou et al. [277] 0.887 0.928 0.823 0.861
    NR Zhou et al. [455] 0.904 0.934 0.890 0.905
    NR Zhou et al. [285] 0.901 0.929 0.819 0.856 0.837 0.867
    FR STRIQE [279] 0.9223 0.9275 0.8920 0.9019
    NR S3D-BLINQ [282] 0.905 0.913
    Feature extraction RR Wang et al. [281] 0.8890 0.8921
    RR Ma-1 et al. [283] 0.9034 0.9033 0.8093 0.8431
    RR Ma-2 et al. [283] 0.9052 0.9056 0.7938 0.8179
    FR 3D-DQE [254] 0.9420 0.9493 0.9449 0.9565 0.9106 0.9265 0.9040 0.9138
    NR Shao et al. [288] 0.9026 0.9061 0.8756 0.9042
    FR Shao et al. [289] 0.9411 0.9413 0.9251 0.9350 0.8494 0.8628
    RR Qi et al. [290] 0.867 0.915
    Sparse representation NR Shao et al. [291] 0.9375 0.9486 0.9440 0.9531 0.8849 0.9034
    NR Shao et al. [292] 0.9305 0.9479 0.9498 0.9572
    NR Shao et al. [294] 0.8667 0.8846 0.8717 0.9095
    NR NUMBLIM [295] 0.8757 0.8679 0.8849 0.8913 0.8054 0.7843

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