Laser & Optoelectronics Progress, Volume. 56, Issue 3, 031102(2019)

A Stitched Image Quality Assessment Method for Color Correction

Meiling Qi and Feng Shao*
Author Affiliations
  • Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang 315211, China
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    Figures & Tables(14)
    Original stitched image sequence. (a) Scene 1; (b) scene 2; (c) scene 6; (d) scene 4; (e) scene 8; (f) scene 3; (g) scene 9; (h) scene 5; (i) scene 7; (j) scene 10
    Flow chart of building a database
    Stitched results of scene 2 in case of color difference 1. (a) Left image; (b) right image; (c) Alg#1; (d) Alg#2; (e) Alg#3; (f) Alg#4; (g) Alg#5; (h) Alg#6; (i) Alg#7; (j) standard image
    Scoring interface
    Distribution of DMOS values
    Flow chart of the proposed method
    Relationship between predicted value of the proposed method and DMOS
    • Table 1. Colorcorrection algorithms

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      Table 1. Colorcorrection algorithms

      Serial numberAlgorithmReference
      Alg#1Brightness functionMethod in Ref. [11]
      Alg#2Brightness and contrast functionsMethod in Ref. [12]
      Alg#3Cumulative histogram mappingMethod in Ref. [13]
      Alg#4Different color emotion transfer functionMethod in Ref. [14]
      Alg#5Single color emotion transfer functionMethod in Ref. [14]
      Alg#6Global color transferMethod in Ref. [15]
      Alg#7Global color transfer in correlated color spaceMethod in Ref. [16]
    • Table 2. Color difference parameters

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      Table 2. Color difference parameters

      Number of imageBrightnessContrastSaturation
      01-20-99050
      21-40-5020-50
      41-600-6050
      61-80500100
      81-100-30-30-40
    • Table 3. PLCC and SROCC values of the proposed method

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      Table 3. PLCC and SROCC values of the proposed method

      MetricPLCCSROCC
      SRRR0.55470.6076
      FSIMc0.43610.4633
      ΔSIFT0.12710.125
      VSI0.16770.2595
      CSQIA0.66760.6508
    • Table 4. CSIQA scores of 10 scenes processed by different color correction algorithms under color differences condition 2

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      Table 4. CSIQA scores of 10 scenes processed by different color correction algorithms under color differences condition 2

      SceneAlg#1Alg#2Alg#3Alg#4Alg#5Alg#6Alg#7Alg#8
      Scene 10.80840.80930.59090.7839Δ0.79340.8151*0.79130.8066
      Scene 20.77790.74600.71570.54090.50470.80470.9165*0.7854
      Scene 30.71760.79740.8178Δ0.22930.72110.99530.9833Δ0.8333
      Scene 40.82620.83530.41520.54310.30200.72900.72900.8006
      Scene 50.8315*0.65200.58330.39720.48010.78370.73250.7417
      Scene 60.74290.77780.80170.59730.66880.78010.8248*0.7183
      Scene 70.85690.82200.58830.63790.8185Δ0.85310.79450.8304
      Scene 80.70870.70000.73760.69630.72940.8630*0.55710.7155
      Scene 90.71320.73000.35120.58800.63570.77040.7938*0.7635
      Scene 100.69550.7824*0.53320.76100.75420.75510.45060.7706
    • Table 5. CSIQA differences between different color correction algorithms and benchmark algorithmsin different scenes

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      Table 5. CSIQA differences between different color correction algorithms and benchmark algorithmsin different scenes

      SceneAlg#1Alg#2Alg#3Alg#4Alg#5Alg#6Alg#7Alg#8
      Scene 10.00180.0027-0.2157-0.0228-0.01320.0084-0.01530.0000
      Scene 2-0.0075-0.0394-0.0696-0.2445-0.28070.01930.13110.0000
      Scene 3-0.1157-0.0359-0.0155-0.6039-0.11220.16200.15000.0000
      Scene 40.02560.0347-0.3854-0.2575-0.4986-0.0716-0.07160.0000
      Scene 50.0898-0.0897-0.1584-0.3445-0.26160.0420-0.00920.0000
      Scene 60.02470.05950.0835-0.1209-0.04950.06190.10660.0000
      Scene 70.0265-0.0084-0.2422-0.1925-0.01190.0227-0.03600.0000
      Scene 8-0.0068-0.01560.0221-0.01920.01390.1475-0.15850.0000
      Scene 9-0.0503-0.0335-0.4123-0.1755-0.12780.00690.03030.0000
      Scene 10-0.07510.0118-0.2374-0.0096-0.0164-0.0155-0.32000.0000
    • Table 6. CSIQA values with different color differences in same scene

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      Table 6. CSIQA values with different color differences in same scene

      Color differenceAlg#1Alg#2Alg#3Alg#4Alg#5Alg#6Alg#7Alg#8
      Color difference 10.45510.60520.70470.40820.42760.91290.94810.5635
      Color difference 20.77790.74600.71570.54090.50470.80470.91650.7854
      Color difference 30.29420.40810.70840.29240.44940.66280.68900.5286
      Color difference 40.33800.40590.69790.41480.40620.79120.85870.8304
      Color difference 50.49140.65410.70820.62260.68080.90610.86000.7213
    • Table 7. CSIQA differences under different color differences

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      Table 7. CSIQA differences under different color differences

      Color differenceAlg#1Alg#2Alg#3Alg#4Alg#5Alg#6Alg#7Alg#8
      Color difference 1-0.10840.04170.1412-0.1553-0.13590.34940.38460.0000
      Color difference 2-0.0075-0.0394-0.0696-0.2445-0.28070.01930.13110.0000
      Color difference 3-0.2344-0.12050.1798-0.2362-0.07930.13410.16040.0000
      Color difference 4-0.4924-0.4245-0.1325-0.4156-0.4243-0.03920.02830.0000
      Color difference 5-0.2299-0.0672-0.0131-0.0987-0.04050.18470.13870.0000
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    Meiling Qi, Feng Shao. A Stitched Image Quality Assessment Method for Color Correction[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031102

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    Paper Information

    Category: Imaging Systems

    Received: Jul. 27, 2018

    Accepted: Aug. 28, 2018

    Published Online: Jul. 31, 2019

    The Author Email: Feng Shao (shaofeng@nbu.edu.cn)

    DOI:10.3788/LOP56.031102

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