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Note: Please write to sumohana ‘at’ iith.ac.in for password information.

Code

  1. A. Uggi, S. S. Channappayya, MS-NetVLAD: Multi-Scale NetVLAD for Visual Place Recognition, IEEE Signal Processing Letters, DOI: 10.1109/LSP.2024.3425279.

  2. P. Kancharla, S. S. Channappayya, Completely Blind Quality Assessment of User Generated Video Content, IEEE Transactions on Image Processing, DOI:10.1109/TIP.2021.3130541.

  3. P. Kancharla, S. S. Channappayya, Improving the Visual Quality of Video Frame Prediction Models Using the Perceptual Straightening Hypothesis, IEEE Signal Processing Letters, DOI: 10.1109/LSP.2021.3118639.

  4. S. V. R. Dendi, C. Dev, N. Kothari, S. S. Channappayya, LQAID: Localized Quality Aware Image Denoising using Deep Convolutional Neural Networks, Proc. ICASSP 2020, Bacelona, Spain.

  5. S. V. R. Dendi, S. S. Channappayya, No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics, IEEE Transactions on Image Processing, 2020, DOI: 10.1109/TIP.2020.2984879.

  6. P. Kancharla, S. S. Channappayya, Quality Aware Generative Adversarial Networks, Thirty Third Neural Information Processing Systems (NeurIPS), 2019.

  7. B. Appina, S. V. R. Dendi, K. Manasa, S. S. Channappayya, A. C. Bovik, Study of Subjective Quality and Objective Blind Quality Prediction of Stereoscopic Videos, IEEE Transactions on Image Processing, 2019, DOI: 10.1109/TIP.2019.2914950.

  8. N. Eswara, S. Ashique, A. Panchbhai, S. Chakraborty, H. P. Sethuram, K. Kuchi, A. Kumar, S. S. Channappayya, Streaming video qoe modeling and prediction: a long short-term memory approach, IEEE Transactions on Circuits and Systems for Video Technology DOI:10.1109/TCSVT.2019.2895223.

  9. S. V. R. Dendi, C. Dev, N. Kothari, S. S. Channappayya, Generating Image Distortion Maps Using Convolutional Autoencoders with Application to No Reference Image Quality Assessment, IEEE Signal Processing Letters DOI: 10.1109/LSP.2018.2879518.

  10. S. Khan Md., S. S. Channappayya, Estimating Depth-Salient Edges And Its Application To Stereoscopic Image Quality Assessment, IEEE Transactions on Image Processing DOI: 10.1109/TIP.2018.2860279.

  11. B. Appina, S. S. Channappayya, Full-Reference 3D Video Quality Assessment Using Scene Component Statistical Dependencies, IEEE Signal Processing Letters DOI: 10.1109/LSP.2018.2829107.

  12. Manasa K., S. S. Channappayya, An Optical Flow-Based Full Reference Video Quality Assessment Algorithm, IEEE Transactions on Image Processing DOI:10.1109/TIP.2016.2548247.

  13. B. Appina, S. Khan Md, S. S. Channappayya, No-reference Stereoscopic Image Quality Assessment Using Natural Scene Statistics, Signal Processing: Image Communication, Volume 43, April 2016, Pages 1-14, DOI:10.1016/j.image.2016.02.001.

  14. S. Khan Md, B. Appina, S. S. Channappayya, Full-reference Stereo Image Quality Assessment Using Natural Stereo Scene Statistics, IEEE Signal Processing Letters, DOI:10.1109/LSP.2015.2449878.

Datasets

  1. Aerial Path Planning Dataset. Please cite the following work when you use this dataset.
    Md. Shahid, S. S. Channappayya, “Aerial Cross-platform Path Planning Dataset,” Proc. ICCV Workshop on Analysis of Aerial Motion Imagery (WAAMI) 2021.

  2. The LFOVIAS3DPh2 dataset. Please cite the following works when you use our dataset.
    B. Appina, S. V. R. Dendi, K. Manasa, S. S. Channappayya, A. C. Bovik, “Study of Subjective Quality and Objective Blind Quality Prediction of Stereoscopic Videos,” IEEE Transactions on Image Processing. DOI: 10.1109/TIP.2019.2914950.
    E. Cheng, P. Burton, J. Burton, A. Joseski, and I. Burnett, “Rmit3dv: Pre-announcement of a creative commons uncompressed HD 3D video database,” in Fourth International Workshop on Quality of Multimedia Experience (QoMEX), pp. 212–217, July 2012.

  3. The LFOVIAS3DPh1 dataset. Please cite this work when you use this database: B. Appina, K. Manasa, S. S. Channappayya, “Subjective and objective study of the relation between 3D and 2D views based on depth and bitrate,” IS & T Electronic Imaging 2017, Burlingame, CA, USA, January 2017.

  4. LFOVIA Video QoE Database. With an increasing demand for online video viewing, understanding and monitoring the end user perceptual Quality-of-Experience (QoE) is essential for optimizing video streaming. With an aim towards building models for effective QoE prediction and enabling QoE-optimizations for video streaming, we present the LFOVIA QoE database. The LFOVIA QoE database provides 54 videos at Full High Definition (2K) and Ultra High Definition (4K) resolutions along with continuous time subjective QoE scores and an overall QoE score for each of these videos. Out of 54 videos, 18 are pristine videos that encompass a wide variety in content, from which the remaining 36 distorted videos are derived. The distorted videos include various QoE influencing patterns that are caused by a combination of time-varying quality due of rate adaptation, and rebuffering events as typically encountered in video streaming.

    Reference Videos: Part 1 of 2, Part 2 of 2.
    Test Videos: Part 1 of 4, Part 2 of 4, Part 3 of 4, Part 4 of 4.
    Video Data: QoE matfiles, README.
    SVR-QoE (code): SVR-QoE.

    Please cite the following work if you use this database:
    N. Eswara, Manasa K., A. Kommineni, S. Chakraborty, H. P. Sethuram, K. Kuchi, A. Kumar, S. S. Channappayya, “A Continuous QoE Evaluation Framework for Video Streaming over HTTP,” IEEE Transactions on Circuits and Systems for Video Technology (CSVT), DOI:10.1109/TCSVT.2017.2742601.