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Laboratory for Image & Video Engineering

Image & Video Quality Assessment Algorithms

Video Processing Toolbox in Python

On the Robust Performance of the ST-RRED Video Quality Predictor

Image and Video Quality Assessment Databases


Software Releases - Please visit our GitHub page (NEW!) for latest releases.

  • DisQUE: Joint Full-Reference Image Quality Assessment and Tunable Image Processing NEW!
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    A. K. Venkataramanan, C. Stejerean, I. Katsavounidis, H. Tmar and A. C. Bovik, "Joint Quality Assessment and Example-Guided Image Processing by Disentangling Picture Appearance from Content," arXiv preprint arXiv:2404.13484, 2024.
  • Cut-FUNQUE: Full-Reference Video Quality Assessment of Compressed Tone-Mapped HDR Videos NEW!
    [GitHub]
    Please cite the following papers in any published work if you use this software.
    A. K. Venkataramanan, C. Stejerean, I. Katsavounidis, H. Tmar and A. C. Bovik, "Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos," arXiv preprint arXiv:2404.13452, 2024.
  • FUNQUE+: Efficient and Accurate Full-Reference Video Quality Assessment NEW!
    [GitHub]
    Please cite the following papers in any published work if you use this software.
    A. K. Venkataramanan, C. Stejerean, I. Katsavounidis and A. C. Bovik, "One Transform to Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment," in IEEE Transactions on Image Processing, vol. 33, pp. 509-524, 2024, doi: 10.1109/TIP.2023.3345227.
    A. K. Venkataramanan, C. Stejerean and A. C. Bovik, "FUNQUE: Fusion of Unified Quality Evaluators," 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 2022, pp. 2147-2151, doi: 10.1109/ICIP46576.2022.9897312.
  • Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild NEW!
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    A. Saha, S. Mishra and A. C. Bovik, "Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild," 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 2023, pp. 5846-5855, doi: 10.1109/CVPR52729.2023.00566.
  • CONVIQT: Contrastive Video Quality Estimator NEW!
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli and A. C. Bovik, "CONVIQT: Contrastive Video Quality Estimator," in IEEE Transactions on Image Processing, vol. 32, pp. 5138-5152, 2023, doi: 10.1109/TIP.2023.3310344.
  • GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    Y. -C. Chen, A. Saha, C. Davis, B. Qiu, X. Wang R. Gowda, I. Katsavounidis and A. C. Bovik, "GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content," in IEEE Signal Processing Letters, vol. 30, pp. 324-328, 2023, doi: 10.1109/LSP.2023.3255011.
  • HDRMAX: Making Video Quality Assessment Models Robust to Bit Depth
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in 2022 IEEE International Conference on Image Processing (ICIP), 2022
    J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman and A. C. Bovik, "Making Video Quality Assessment Models Robust to Bit Depth," in IEEE Signal Processing Letters, vol. 30, pp. 488-492, 2023, doi: 10.1109/LSP.2023.3268602.
    Z. Shang et al., "A Study of Subjective and Objective Quality Assessment of HDR Videos," in IEEE Transactions on Image Processing, vol. 33, pp. 42-57, 2024, doi: 10.1109/TIP.2023.3333217.
  • CONTRIQUE: Image Quality Assessment using Contrastive Learning
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli and A. C. Bovik, "Image Quality Assessment Using Contrastive Learning," in IEEE Transactions on Image Processing, vol. 31, pp. 4149-4161, 2022, doi: 10.1109/TIP.2022.3181496.
  • Enhanced SSIM: An Improved Structural Similarity Metric
    [GitHub] [GitHub (Optimized)]
    Please cite the following papers in any published work if you use this software.
    A. K. Venkataramanan, C. Wu, A. C. Bovik, I. Katsavounidis and Z. Shahid, "A Hitchhiker's Guide to Structural Similarity," in IEEE Access, vol. 9, pp. 28872-28896, 2021, doi: 10.1109/ACCESS.2021.3056504.
  • RAPIQUE: Rapid and Accurate Video Quality Evaluator
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    Z. Tu, X. Yu, Y. Wang, N. Birkbeck, B. Adsumilli and A. C. Bovik, "RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content," in IEEE Open Journal of Signal Processing, vol. 2, pp. 425-440, 2021, doi: 10.1109/OJSP.2021.3090333.
    Z. Tu, C. -J. Chen, Y. Wang, N. Birkbeck, B. Adsumilli and A. C. Bovik, "Efficient User-Generated Video Quality Prediction," 2021 Picture Coding Symposium (PCS), 2021, pp. 1-5, doi: 10.1109/PCS50896.2021.9477483.
  • BBAND/AdaDeband: banding artifact detector and remover
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    Z. Tu, J. Lin, Y. Wang, B. Adsumilli and A. C. Bovik, "BBAND INDEX: A NO-REFERENCE BANDING ARTIFACT PREDICTOR," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 2712-2716, doi: 10.1109/ICASSP40776.2020.9053634.
    Z. Tu, J. Lin, Y. Wang, B. Adsumilli and A. C. Bovik, "Adaptive Debanding Filter," in IEEE Signal Processing Letters, vol. 27, pp. 1715-1719, 2020, doi: 10.1109/LSP.2020.3024985.
  • ChipQA-0: No-Reference Video Quality Assessment Using Space-Time Chips
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei and A. C. Bovik, "No-Reference Video Quality Assessment Using Space-Time Chips," 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), 2020, pp. 1-6, doi: 10.1109/MMSP48831.2020.9287151.
  • VIDEVAL: Feature Fused Video Quality Evaluator
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    Z. Tu, Y. Wang, N. Birkbeck, B. Adsumilli and A. C. Bovik, "UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content," in IEEE Transactions on Image Processing, vol. 30, pp. 4449-4464, April, 2021, doi: 10.1109/TIP.2021.3072221
  • ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction
    [GitHub]
    Please cite the following paper in any published work if you use this software.
    Madhusudana, Pavan C., Neil Birkbeck, Yilin Wang, Balu Adsumilli, and Alan C. Bovik. "ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction." arXiv preprint arXiv:2010.13715 (2020).
  • Patch-VQ: ‘Patching Up’ the Video Quality Problem
    [Project Website] [GitHub] [Database]
    Please cite the following paper in any published work if you use this software.
    Z. Ying, M. Mandal, D. Ghadiyaram, and A.C. Bovik, “Patch-VQ: ‘Patching Up’ the Video Quality Problem,” arXiv:2011.13544, Nov. 2020.
  • Audio-visual versions of SSIM, MS-SSIM, VIFP, GMSM, GMSD, and VMAF
    [zip] [GitHub]
    Please cite the following paper in any published work if you use this software.
    X. Min, G. Zhai, J. Zhou, M. C. Q. Farias, and A. C. Bovik, "Study of Subjective and Objective Quality Assessment of Audio-Visual Signals," IEEE Transactions on Image Processing, vol. 29, pp. 6054-6068, 2020.
  • From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality
    [Project Website] [GitHub] [Database]
    Please cite the following paper in any published work if you use this software.
    Z. Ying, H. Niu, P. Gupta, D. Mahajan, D. Ghadiyaram, and A.C. Bovik, “From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality,” arXiv:1912.10088, Dec. 2019.
  • 2stepQA: Predicting the Quality of Images Compressed After Distortion in Two Steps
    [zip] [GitHub]
    Please cite the following paper in any published work if you use this software.
    X. Yu, C. G. Bampis, P. Gupta and A. C. Bovik, "Predicting the Quality of Images Compressed After Distortion in Two Steps", IEEE Transactions on Image Processing, vol. 28, no. 12, pp. 5757-5770, December 2019.
  • SpEED-QA: Spatial Efficient Entropic Differencing for Image and Video Quality
    [zip] [GitHub]
    Please cite the following paper in any published work if you use this software.
    C. G. Bampis, P. Gupta, R. Soundararajan and A. C. Bovik, "SpEED-QA: Spatial Efficient Entropic Differencing for Image and Video Quality," in IEEE Signal Processing Letters, vol. 24, no. 9, pp. 1333-1337, Sept. 2017.
  • Binocular spatial activity and reverse saliency driven no-reference stereopair quality assessment
    Download here. [GitHub]
    Please cite the following paper in any published work if you use this software.
    L. Liu, B. Liu, C. C. Su, H. Huang, A. C. Bovik, "Binocular spatial activity and reverse saliency driven no-reference stereopair quality assessment", Signal Processing: Image Communication, 2017.
  • HDR Image GRADient based Evaluator (HIGRADE)
    Download here. [GitHub]
    Please cite the following paper in any published work if you use this software.
    D. Kundu, D. Ghadiyaram, A.C. Bovik and B.L. Evans, “No-reference quality assessment of high dynamic range pictures,” IEEE Transactions on Image Processing, to appear.
  • Blind image quality assessment by relative gradient statistics and Adaboosting neural network
    Download here. [GitHub]
    Please cite the following paper in any published work if you use this software.
    L. Liu, Y. Hua, Q. Zhao, H. Huang and A.C. Bovik, "Blind image quality assessment by relative gradient statistics and Adaboosting neural network," Signal Processing: Image Communication , vol. 40, no. 1, pp. 1-15, January, 2016
  • Feature maps based Referenceless Image QUality Evaluation Engine (FRIQUEE) NEW!
    Download here. [GitHub]
    Please cite the following paper in any published work if you use this software.
    D. Ghadiyaram and A. C. Bovik, "Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach," http://arxiv.org/abs/1609.04757 (under review)
  • Gradient Magnitude Similarity Deviation (GMSD). Download here [GitHub]
    Please cite the following paper in any published work if you use this software.
    • W. Xue, L. Zhang, X. Mou and A.C. Bovik, “Gradient magnitude similarity deviation: A highly efficient perceptual image quality index,” IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 684-695, February 2014
  • IL-NIQE Download here
    Please cite the following paper in any published work if you use this software.
    • L. Zhang, L. Zhang and A.C. Bovik, “A feature-enriched completely blind local image quality analyzer,” IEEE Transactions on Image Processing, vol. 24, no. 8, pp. 2579-2591, August 2015.
  • GM-LOG-BIQA Download here
    Please cite the following paper in any published work if you use this software.
    • W. Xue, X. Mou, L. Zhang, A.C. Bovik, and X. Feng, “Blind image quality prediction using joint statistics of gradient magnitude and laplacian features,” IEEE Transactions on Image Processing, vol. 23, no. 11, pp. 4850-4862, November 2014.
  • A Completely Blind Video Integrity Oracle (VIIDEO)
    Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    A. Mittal, M. A. Saad, and A. C. Bovik, "A Completely Blind Video Integrity Oracle," IEEE Transactions on Image Processing , vol. 25, no. 1, pp. 289-300, January, 2016.
  • No-reference Image Quality Assessment based on Spatial and Spectral Entropies
    Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    L. Liu, B. Liu, H. Huang, and A.C. Bovik “ No-reference image quality assessment based on spatial and spectral entropies,” Signal Processing: Image Communication, June 2014
  • Blind prediction of natural video quality
    Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    M. Saad and A.C. Bovik, “ Blind prediction of natural video quality ” IEEE Transactions on Image Processing, December 2013
  • No-reference image quality assessment in curvelet domain
    Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    Lixiong Liu, Hongping Dong, Hua Huang, Alan C. Bovik, “ No-reference image quality assessment in curvelet domain ” Signal Processing: Image Communication, February 2014.
  • Making image quality assessment robust
    Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    A. Mittal, A. K. Moorthy and A. C. Bovik, “ Making image quality assessment robust ” Forty-Sixth Annual Asilomar Conference on Signals, Systems, and Computers, Monterey, California, November 04-07, 2012
  • Topic model based Image Quality Assessment NEW!
    Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    A.Mittal, G.S.Muralidhar, J.Ghosh and A.C. Bovik, “Blind image quality assessment without human training using latent quality factors ” IEEE Signal Processing Letters", pp 75-78, Vol 19, no 2, February 2012.
  • Full-reference quality assessment of stereopairs accounting for rivalry NEW!
    Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    Ming-Jun Chen, Che-Chun Su, Do-Kyoung Kwon, Lawrence K. Cormack, Alan C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry, ” Signal Processing: Image Communication, 2013.
  • Naturalness Image Quality Evaluator (NIQE)
    Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    A. Mittal, R. Soundararajan and A. C. Bovik, “Making a Completely Blind Image Quality Analyzer, ” IEEE Signal Processing Letters , pp. 209-212, vol. 22, no. 3, March 2013.
  • Fast Structural Similarity Index algorithm
    The link of c++ code: Download here.
    The link of binary: Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    M.J. Chen and A.C. Bovik, “Fast structural similarity index algorithm, ” Journal of Real-Time Image Processing, Vol: 6 No: 4, Page(s): 281-287, December , 2011.
  • Blind/Referenceless Image Spatial QUality Evaluator (BRISQUE)
    Download the Matlab code here.
    Download the C++ Version here. [GitHub] NEW!
    Please cite the following papers in any published work if you use this software.
    A. Mittal, A. K. Moorthy and A. C. Bovik, “No-Reference Image Quality Assessment in the Spatial Domain,” IEEE Transactions on Image Processing , 2012 (to appear).
    A. Mittal, A. K. Moorthy and A. C. Bovik, “Referenceless Image Spatial Quality Evaluation Engine,” 45th Asilomar Conference on Signals, Systems and Computers , November 2011.
  • GRNN based No Reference image Quality Index (GRNN-NRQI)
    Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    Chaofeng li, Alan Bovik and Xiaojun Wu. Blind Image Quality Assessment Using a General Regression Neural Network, IEEE Transactions on Neural Networks, 22(5), 2011. 793-799.
  • BLind Image Integrity Notator using DCT Statistics - II (BLIINDS-II) Index
    Download here. [GitHub]
    Please cite the following papers in any published work if you use this software.
    M.A Saad and A. C. Bovik, "Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain, " IEEE Transactions
    on Image Processing
    , vol. pp, no. 99, pp. 1, Mar. 2012. (early access)
    M.A. Saad, A. C. Bovik, and C. Charrier, "DCT Statistics Model-Based Blind Image Quality Assessment," Proceedings of the IEEE International Conference on image Processing (ICIP) , pp. 3093-3096, Sep. 2011.
  • Reduced Reference Entropic Differencing (RRED) Index
    Download here. [GitHub]
    Please cite the following paper in any published work if you use this software.
    R. Soundararajan and A. C. Bovik, "RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment," IEEE Transactions on Image Processing , vol. 21, no. 2, pp. 517-526, Feb. 2012.
  • Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) Index
    Download here. [GitHub]
    Please cite the following paper in any published work if you use this software.
    A. K. Moorthy and A. C. Bovik, ``Blind Image Quality Assessment: From Scene Statistics to Perceptual Quality'', IEEE Transactions Image Processing , pp. 3350-3364, vol. 20, no. 12, December 2011.
  • Blind Image Quality Index (BIQI).
    Download here. [GitHub]
    Please cite the following paper in any published work if you use this software.
    A. K. Moorthy and A. C. Bovik, "A Two-Step Framework for Constructing Blind Image Quality Indices", IEEE Signal Processing Letters , pp. 513-516, vol. 17, no. 5, May 2010.
  • MOtion-based Video Integrity Evaluation (MOVIE) index for video quality assessment.
    Download here. [GitHub]
    Please cite the following paper in any published work if you use this software.
    • Kalpana Seshadrinathan and A.C. Bovik, "Motion Tuned Spatio-temporal Quality Assessment of Natural Videos," vol. 19, no. 2, pp. 335-350, IEEE Transactions on Image Processing , Feb. 2010.
  • BLIINDS: BLind Image Integrity Notator using DCT Statistics (Matlab Code). Download here. [GitHub]
    Please cite the following paper in any published work if you use this software.
    M.A. Saad, A.C. Bovik and C. Charrier, "A DCT Statistics-Based Blind Image Quality Index", IEEE Signal Processing Letters , pp. 583-586, vol. 17, no. 6, June 2010.
  • Visual Information Fidelity (VIF) measure for Image Quality Assessment. Download here. [GitHub]
    You need Steerable Pyramid toolbox to run it. Please cite the following paper in any published work if you use this software.
    • H.R. Sheikh.and A.C. Bovik, "Image information and visual quality," IEEE Transactions on Image Processing , vol.15, no.2,pp. 430- 444, Feb. 2006.
  • Pixel domain version of VIF. Download here.
    A computationally simpler, multi-scale pixel domain implementation of VIF whose performance is slightly worse than the Wavelet domain version. Read More .Please cite the following paper in any published work if you use this software.
    • H.R. Sheikh.and A.C. Bovik, "Image information and visual quality," IEEE Transactions on Image Processing , vol.15, no.2,pp. 430- 444, Feb. 2006.
  • Information Fidelity Criterion for Image Quality Assessment. Download here. [GitHub]
    Note: Visual Information Fidelity (VIF) is an extension and improvement. You need Steerable Pyramid toolbox to run it. Please cite the following paper in any published work if you use this software.
    • H.R. Sheikh, A.C. Bovik and G. de Veciana, "An information fidelity criterion for image quality assessment using natural scene statistics," IEEE Transactions on Image Processing , vol.14, no.12pp. 2117- 2128, Dec. 2005.
  • No Reference Image quality assessment for JPEG2000. Download here [GitHub] .
    Please cite the following paper in any published work if you use this software.
    • H. R. Sheikh, A. C. Bovik, and L. K. Cormack, "No-Reference Quality Assessment Using Natural Scene Statistics: JPEG2000," IEEE Transactions on Image Processing , vol. 14, no. 12, December 2005.
  • Structural Similarity Index (SSIM). Download LabView implementation here [GitHub] .
    Visit SSIM's Matlab page for a Matlab implementation of the metric. Please cite the following paper in any published work if you use this software.
    • Z. Wang, A.C. Bovik, H.R. Sheikh and E.P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing , vol.13, no.4pp. 600- 612, April 2004.
  • Multi-Scale Structural Similarity Index (MS-SSIM). Download here [GitHub] .
    Please cite the following paper in any published work if you use this software.
    • Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," IEEE Asilomar Conference Signals, Systems and Computers , Nov. 2003.

Image and Video Quality Assessment Links