task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Image Quality Assessment > Full reference image quality assessment | ESPL | MDSI | http://arxiv.org/abs/1608.07433v4 | SRCC | 0.8806 |
Image Quality Assessment > Full reference image quality assessment | ESPL | MDSI | http://arxiv.org/abs/1608.07433v4 | PLCC | 0.8802 |
Image Quality Assessment > Full reference image quality assessment | ESPL | SR-SIM | https://www.semanticscholar.org/paper/SR-SIM%3A-A-fast-and-high-performance-IQA-index-based-Zhang-Li/9f962cbb9c51e5d2827e495cbb30abcf3c41ecc1 | SRCC | 0.8802 |
Image Quality Assessment > Full reference image quality assessment | ESPL | SR-SIM | https://www.semanticscholar.org/paper/SR-SIM%3A-A-fast-and-high-performance-IQA-index-based-Zhang-Li/9f962cbb9c51e5d2827e495cbb30abcf3c41ecc1 | PLCC | 0.8732 |
Image Quality Assessment > Full reference image quality assessment | ESPL | FSIMc | https://ieeexplore.ieee.org/document/5705575 | SRCC | 0.8766 |
Image Quality Assessment > Full reference image quality assessment | ESPL | FSIMc | https://ieeexplore.ieee.org/document/5705575 | PLCC | 0.8738 |
Image Quality Assessment > Full reference image quality assessment | ESPL | VSI | https://ieeexplore.ieee.org/document/6873260 | SRCC | 0.8717 |
Image Quality Assessment > Full reference image quality assessment | ESPL | VSI | https://ieeexplore.ieee.org/document/6873260 | PLCC | 0.8726 |
Image Quality Assessment > Full reference image quality assessment | ESPL | MAD | https://www.researchgate.net/publication/220050520_Most_apparent_distortion_Full-reference_image_quality_assessment_and_the_role_of_strategy | SRCC | 0.8624 |
Image Quality Assessment > Full reference image quality assessment | ESPL | MAD | https://www.researchgate.net/publication/220050520_Most_apparent_distortion_Full-reference_image_quality_assessment_and_the_role_of_strategy | PLCC | 0.8677 |
Image Quality Assessment > Full reference image quality assessment | ESPL | HaarPSI | http://arxiv.org/abs/1607.06140v4 | SRCC | 0.8510 |
Image Quality Assessment > Full reference image quality assessment | ESPL | HaarPSI | http://arxiv.org/abs/1607.06140v4 | PLCC | 0.8526 |
Image Quality Assessment > Full reference image quality assessment | ESPL | IW-SSIM | https://ieeexplore.ieee.org/document/5635337 | SRCC | 0.8270 |
Image Quality Assessment > Full reference image quality assessment | ESPL | IW-SSIM | https://ieeexplore.ieee.org/document/5635337 | PLCC | 0.8300 |
Image Quality Assessment > Full reference image quality assessment | ESPL | GMSD | http://arxiv.org/abs/1308.3052v2 | SRCC | 0.8209 |
Image Quality Assessment > Full reference image quality assessment | ESPL | GMSD | http://arxiv.org/abs/1308.3052v2 | PLCC | 0.8234 |
Image Quality Assessment > Full reference image quality assessment | ESPL | SFF | https://ieeexplore.ieee.org/document/6525380/authors#authors | SRCC | 0.8127 |
Image Quality Assessment > Full reference image quality assessment | ESPL | SFF | https://ieeexplore.ieee.org/document/6525380/authors#authors | PLCC | 0.8179 |
Image Quality Assessment > Full reference image quality assessment | ESPL | VIF | https://ieeexplore.ieee.org/document/1576816 | SRCC | 0.7488 |
Image Quality Assessment > Full reference image quality assessment | ESPL | VIF | https://ieeexplore.ieee.org/document/1576816 | PLCC | 0.7423 |
Image Quality Assessment > Full reference image quality assessment | ESPL | MS-SSIM | https://ieeexplore.ieee.org/document/1292216 | SRCC | 0.7247 |
Image Quality Assessment > Full reference image quality assessment | ESPL | MS-SSIM | https://ieeexplore.ieee.org/document/1292216 | PLCC | 0.7322 |
Image Quality Assessment > Aesthetics Quality Assessment | AVA | MP_adam | https://www.researchgate.net/publication/328371233_Attention-based_Multi-Patch_Aggregation_for_Image_Aesthetic_Assessment | Accuracy | 83.0% |
Image Quality Assessment > Aesthetics Quality Assessment | AVA | A-Lamp | http://arxiv.org/abs/1704.00248v1 | Accuracy | 82.5% |
Image Quality Assessment > Aesthetics Quality Assessment | AVA | Pool-3FC | http://arxiv.org/abs/1904.01382v1 | Accuracy | 81.7% |
Image Quality Assessment > Aesthetics Quality Assessment | AVA | NIMA | http://arxiv.org/abs/1709.05424v2 | Accuracy | 81.5% |
Image Quality Assessment > Aesthetics Quality Assessment | AVA | MTRLCNN | http://arxiv.org/abs/1604.04970v3 | Accuracy | 79.1% |
Image Quality Assessment > Aesthetics Quality Assessment | AVA | MNA-CNN | http://openaccess.thecvf.com/content_cvpr_2016/html/Mai_Composition-Preserving_Deep_Photo_CVPR_2016_paper.html | Accuracy | 77.4% |
Image Quality Assessment > Aesthetics Quality Assessment | AVA | ADB-CNN | http://arxiv.org/abs/1606.01621v2 | Accuracy | 77.3% |
Image Quality Assessment > Aesthetics Quality Assessment | AVA | DMA-Net | http://openaccess.thecvf.com/content_iccv_2015/html/Lu_Deep_Multi-Patch_Aggregation_ICCV_2015_paper.html | Accuracy | 75.4% |
Image Quality Assessment > Aesthetics Quality Assessment | AVA | Hand-crafted features | null | Accuracy | 68.0% |
Image Quality Assessment > Aesthetics Quality Assessment | Aesthetic Visual Analysis | RvTC+ | https://arxiv.org/abs/2507.14997 | SRCC | 0.899 |
Image Quality Assessment > Aesthetics Quality Assessment | Aesthetic Visual Analysis | RvTC+ | https://arxiv.org/abs/2507.14997 | PLCC | 0.901 |
Image Quality Assessment > Aesthetics Quality Assessment | Aesthetic Visual Analysis | RvTC (image-only) | https://arxiv.org/abs/2507.14997 | SRCC | 0.833 |
Image Quality Assessment > Aesthetics Quality Assessment | Aesthetic Visual Analysis | RvTC (image-only) | https://arxiv.org/abs/2507.14997 | PLCC | 0.831 |
Image Quality Assessment > Aesthetics Quality Assessment | Aesthetic Visual Analysis | OneAlign | https://arxiv.org/abs/2312.17090v1 | SRCC | 0.823 |
Image Quality Assessment > Aesthetics Quality Assessment | CADB | SAMP-Net (Ours) | https://arxiv.org/abs/2104.03133v2 | MSE | 0.3867 |
Image Quality Assessment > Aesthetics Quality Assessment | CADB | SAMP-Net (Ours) | https://arxiv.org/abs/2104.03133v2 | EMD | 0.1798 |
Image Quality Assessment > Aesthetics Quality Assessment | CADB | SAMP-Net (Ours) | https://arxiv.org/abs/2104.03133v2 | SRCC | 0.6564 |
Image Quality Assessment > Aesthetics Quality Assessment | CADB | SAMP-Net (Ours) | https://arxiv.org/abs/2104.03133v2 | LCCAll | 0.6709 |
Image Quality Assessment > Aesthetics Quality Assessment | Image Aesthetics dataset | OrdinalCLIP | https://arxiv.org/abs/2206.02338v2 | Accuracy | 73.05 |
Image Quality Assessment > Aesthetics Quality Assessment | Image Aesthetics dataset | OrdinalCLIP | https://arxiv.org/abs/2206.02338v2 | MAE | 0.280 |
Image Quality Assessment > Aesthetics Quality Assessment | Image Aesthetics dataset | POE | https://arxiv.org/abs/2103.13629v1 | Accuracy | 72.44 |
Image Quality Assessment > Aesthetics Quality Assessment | Image Aesthetics dataset | POE | https://arxiv.org/abs/2103.13629v1 | MAE | 0.287 |
Image Quality Assessment > Aesthetics Quality Assessment | Image Aesthetics dataset | SORD | http://openaccess.thecvf.com/content_CVPR_2019/html/Diaz_Soft_Labels_for_Ordinal_Regression_CVPR_2019_paper.html | Accuracy | 72.03 |
Image Quality Assessment > Aesthetics Quality Assessment | Image Aesthetics dataset | SORD | http://openaccess.thecvf.com/content_CVPR_2019/html/Diaz_Soft_Labels_for_Ordinal_Regression_CVPR_2019_paper.html | MAE | 0.290 |
Image Quality Assessment > Aesthetics Quality Assessment | Image Aesthetics dataset | CNNPOR | http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_A_Constrained_Deep_CVPR_2018_paper.html | Accuracy | 70.05 |
Image Quality Assessment > Aesthetics Quality Assessment | Image Aesthetics dataset | CNNPOR | http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_A_Constrained_Deep_CVPR_2018_paper.html | MAE | 0.316 |
Image Enhancement | MIT-Adobe FiveK | TreEnhance | https://arxiv.org/abs/2205.12639v2 | LPIPS | 0.06 |
Image Enhancement | MIT-Adobe FiveK | TreEnhance | https://arxiv.org/abs/2205.12639v2 | PSNR | 21.24 |
Image Enhancement | MIT-Adobe FiveK | TreEnhance | https://arxiv.org/abs/2205.12639v2 | DeltaE | 11.25 |
Image Enhancement | MIT-Adobe FiveK | TreEnhance | https://arxiv.org/abs/2205.12639v2 | SSIM | 0.89 |
Image Enhancement | TIP 2018 | ESDNet-L | https://arxiv.org/abs/2207.09935v1 | PSNR | 30.11 |
Image Enhancement | TIP 2018 | ESDNet-L | https://arxiv.org/abs/2207.09935v1 | SSIM | 0.920 |
Image Enhancement | TIP 2018 | MBCNN | https://arxiv.org/abs/2004.00406v1 | PSNR | 30.03 |
Image Enhancement | TIP 2018 | MBCNN | https://arxiv.org/abs/2004.00406v1 | SSIM | 0.893 |
Image Enhancement | TIP 2018 | ESDNet | https://arxiv.org/abs/2207.09935v1 | PSNR | 29.81 |
Image Enhancement | TIP 2018 | ESDNet | https://arxiv.org/abs/2207.09935v1 | SSIM | 0.916 |
Image Enhancement | TIP 2018 | Uformer-B | https://arxiv.org/abs/2106.03106v2 | PSNR | 29.28 |
Image Enhancement | TIP 2018 | Uformer-B | https://arxiv.org/abs/2106.03106v2 | SSIM | 0.917 |
Image Enhancement | TIP 2018 | MopNet | http://openaccess.thecvf.com/content_ICCV_2019/html/He_Mop_Moire_Patterns_Using_MopNet_ICCV_2019_paper.html | PSNR | 27.75 |
Image Enhancement | TIP 2018 | MopNet | http://openaccess.thecvf.com/content_ICCV_2019/html/He_Mop_Moire_Patterns_Using_MopNet_ICCV_2019_paper.html | SSIM | 0.895 |
Image Enhancement | TIP 2018 | DMCNN | http://arxiv.org/abs/1805.02996v1 | PSNR | 26.77 |
Image Enhancement | TIP 2018 | DMCNN | http://arxiv.org/abs/1805.02996v1 | SSIM | 0.871 |
Image Enhancement | TIP 2018 | DMCNN | http://arxiv.org/abs/1805.02996v1 | FSIM | 0.914 |
Image Enhancement | PSNR | Analyzing Noise Models and Advanced Filtering Algorithms for Image Enhancement | https://arxiv.org/abs/2410.21946v2 | PSNR | PSNR Values |
Image Enhancement | MIT-Adobe 5k | Retinexformer | https://arxiv.org/abs/2303.06705v3 | PSNR on proRGB | 25.98 |
Image Enhancement | MIT-Adobe 5k | Retinexformer | https://arxiv.org/abs/2303.06705v3 | SSIM on proRGB | 0.957 |
Image Enhancement | MIT-Adobe 5k | Retinexformer | https://arxiv.org/abs/2303.06705v3 | PSNR on sRGB | 24.94 |
Image Enhancement | MIT-Adobe 5k | Retinexformer | https://arxiv.org/abs/2303.06705v3 | SSIM on sRGB | 0.907 |
Image Enhancement | MIT-Adobe 5k | HG-MTFE | https://ieeexplore.ieee.org/abstract/document/10707348 | PSNR on proRGB | 25.69 |
Image Enhancement | MIT-Adobe 5k | PQDynamicISP | https://arxiv.org/abs/2403.10091v1 | PSNR on proRGB | 25.53 |
Image Enhancement | MIT-Adobe 5k | PQDynamicISP | https://arxiv.org/abs/2403.10091v1 | SSIM on proRGB | 0.928 |
Image Enhancement | MIT-Adobe 5k | AdaInt | https://arxiv.org/abs/2204.13983v1 | PSNR on proRGB | 25.49 |
Image Enhancement | MIT-Adobe 5k | AdaInt | https://arxiv.org/abs/2204.13983v1 | SSIM on proRGB | 0.926 |
Image Enhancement | MIT-Adobe 5k | RSFNet-map | https://arxiv.org/abs/2303.08682v2 | PSNR on proRGB | 25.49 |
Image Enhancement | MIT-Adobe 5k | RSFNet-map | https://arxiv.org/abs/2303.08682v2 | SSIM on proRGB | 0.924 |
Image Enhancement | MIT-Adobe 5k | SepLUT | https://arxiv.org/abs/2207.08351v1 | PSNR on proRGB | 25.47 |
Image Enhancement | MIT-Adobe 5k | SepLUT | https://arxiv.org/abs/2207.08351v1 | SSIM on proRGB | 0.921 |
Image Enhancement | MIT-Adobe 5k | MTFE | https://doi.org/10.1016/j.jvcir.2023.103863 | PSNR on proRGB | 25.46 |
Image Enhancement | MIT-Adobe 5k | 3D LUT | https://arxiv.org/abs/2009.14468v1 | PSNR on proRGB | 25.21 |
Image Enhancement | MIT-Adobe 5k | 3D LUT | https://arxiv.org/abs/2009.14468v1 | SSIM on proRGB | 0.922 |
Image Enhancement | MIT-Adobe 5k | 4D LUT | https://arxiv.org/abs/2209.01749v1 | PSNR on proRGB | 24.61 |
Image Enhancement | MIT-Adobe 5k | 4D LUT | https://arxiv.org/abs/2209.01749v1 | SSIM on proRGB | 0.918 |
Image Enhancement | MIT-Adobe 5k | DIFAR (MSCA, level 1) | https://arxiv.org/abs/1911.13175v4 | PSNR on proRGB | 24.2 |
Image Enhancement | MIT-Adobe 5k | DIFAR (MSCA, level 1) | https://arxiv.org/abs/1911.13175v4 | SSIM on proRGB | 0.88 |
Image Enhancement | MIT-Adobe 5k | DeepLPF | https://arxiv.org/abs/2003.13985v1 | PSNR on proRGB | 23.93 |
Image Enhancement | MIT-Adobe 5k | DeepLPF | https://arxiv.org/abs/2003.13985v1 | SSIM on proRGB | 0.903 |
Image Enhancement | Exposure-Errors | Exposure-slot | https://cvpr.thecvf.com/virtual/2025/poster/33508 | PSNR | 23.18 |
Image Enhancement | Exposure-Errors | Exposure-slot | https://cvpr.thecvf.com/virtual/2025/poster/33508 | SSIM | 0.8697 |
Image Enhancement | Exposure-Errors | CSEC | https://arxiv.org/abs/2405.17725v2 | PSNR | 22.728 |
Image Enhancement | Exposure-Errors | CSEC | https://arxiv.org/abs/2405.17725v2 | SSIM | 0.863 |
Image Enhancement | Exposure-Errors | LCDPNet | https://hywang99.github.io/lcdpnet/ | PSNR | 22.173 |
Image Enhancement | Exposure-Errors | LCDPNet | https://hywang99.github.io/lcdpnet/ | SSIM | 0.851 |
Image Enhancement | Exposure-Errors | IAT | https://arxiv.org/abs/2205.14871v4 | PSNR | 20.34 |
Image Enhancement | Exposure-Errors | IAT | https://arxiv.org/abs/2205.14871v4 | SSIM | 0.844 |
Image Enhancement | Exposure-Errors | MSEC | https://arxiv.org/abs/2003.11596v3 | PSNR | 20.205 |
Image Enhancement | Exposure-Errors | MSEC | https://arxiv.org/abs/2003.11596v3 | SSIM | 0.769 |
Image Enhancement | SICE-Mix | CIDNet | https://arxiv.org/abs/2402.05809v3 | Average PSNR | 13.425 |
Image Enhancement | SICE-Mix | CIDNet | https://arxiv.org/abs/2402.05809v3 | SSIM | 0.636 |
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