Research @ LFOVIA

Image and Video Quality Assessment

The explosive growth of image and video content has put tremendous stress on storage and communication systems. It is imperative to manage this content to make the best use of available resources and to better plan for next generation systems. Toward this end, we believe that the ability to automatically order content based on perceptual quality would be extremely useful for storage and communication. It has been conclusively shown that standard lp type metrics and their functions like PSNR do not correlate well with subjective judgment (see figure for a simple example). We are actively working on developing objective quality assessment algorithms for both image and video content. Quality assessment algorithms can broadly be categorized into full-reference, reduced-reference and no-reference based on the availability of the pristine reference for comparison. From a practical viewpoint, the no-reference case is most relevant and forms the primary focus of our work.

Publications

2D Video Quality Assessment

1. N. S. Mahankali, M. Raghavan, S. S. Channappayya, “No-Reference Video Quality Assessment Using Voxel-wise fMRI Models of the Visual Cortex,” IEEE Signal Processing Letters. DOI: 10.1109/LSP.2021.3136487.

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. S. Mitra, R. Soundararajan, S. S. Channappayya, “Predicting Spatio-temporal Entropic Differences for Robust No Reference Video Quality Assessment,” IEEE Signal Processing Letters. DOI: 10.1109/LSP.2021.3049682.

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

5. Manasa K., S. S. Channappayya, “An Optical Flow-Based Full Reference Video Quality Assessment Algorithm,” IEEE Trans. on Image Processing vol. 25, no. 9, pp 2480-2492, 2016 DOI:10.1109/TIP.2016.2548247.

6. Manasa K., S. S. Channappayya, “An Optical Flow-Based No-Reference Video Quality Assessment Algorithm,” Proc. of IEEE ICIP 2016, Phoenix, AZ, USA, September 2016.

7. Manasa K., K. V. S. N. L. Manasa Priya, S. S. Channappayya, "A Perceptually Motivated No-reference Video Quality Assessment Algorithm for Packet Loss Artifacts,’’ Proc. of QoMEX 2014, Singapore, September 2014.

2D Image Quality Assessment

1. 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. [Abstract, Code].

2. K. V. S. N. L. Manasa Priya, B. Appina, S. S. Channappayya, “No-Reference Image Quality Assessment Using Statistics of Sparse Representations,” Proc. of SPCOM 2016, Bengaluru, June 2016.

3. S. Vignesh, K. V. S. N. L. Manasa Priya, S. S. Channappayya, "Face Image Quality Assessment for Face Selection in Surveillance Video using Convolutional Neural Networks,’’ Proc. of IEEE GlobalSIP 2015, Orlando, FL, USA, December 2015.

4. V. Neeluri, P. Dendi, M. Chandrashekhar Bh., S. S. Channappayya, S. Medasani, “Blind Image Quality Evaluation Using Perception Based Features,” Proc. of NCC 2015, IIT Bombay.

5. K. V. S. N. L. Manasa Priya, S. S. Channappayya, "A Novel Sparsity-inspired Blind Image Quality Assessment Algorithm,’’ Proc. of IEEE GlobalSIP 2014, Atlanta, USA, December 2014.

6. P. Dendi, V. Neeluri, M. Chandrasekar Bh, S. S. Channappayya, S. Medasani, "Blind Distortion Classification Using Content and Perception Based Features,’’ Proc. of QoMEX 2014, Singapore, September 2014.

7. K. V. S. N. L. Manasa Priya, K. Manasa, S. S. Channappayya, "A Statistical Evaluation of Sparsity-based Distance Measure (SDM) as an Image Quality Assessment Algorithm,’’ Proc. of ICASSP 2014, Florence, Italy, May 2014.

3D and Multiview Quality Assessment

1. 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. [Abstract, Code].

2. S. Khan Md., S. S. Channappayya, “Estimating Depth-Salient Edges And Its Application To Stereoscopic Image Quality Assessment,” IEEE Trans. on Image Processing DOI: 10.1109/TIP.2018.2860279. [Abstract, Code].

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

4. R. R. Tamboli, B. Appina, S. S. Channappayya, S. Jana, “Super-Multiview Content with High Angular Resolution: 3D Quality Assessment on Horizontal-Parallax Lightfield Display,” Signal Processing: Image Communication, doi:10.1016/j.image.2016.05.010.

5. Appina Balasubramanyam, Sameeulla 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.

6. Sameeulla Khan Md, Appina Balasubramanyam, S. S. Channappayya, “Full-reference Stereo Image Quality Assessment Using Natural Stereo Scene Statistics,” IEEE Signal Processing Letters, vol. 22, no. 11, pp 1985-1989, 2015 DOI: 10.1109/LSP.2015.2449878.

7. B. Appina, K. Manasa, S. S. Channappayya, “A Full Reference Stereoscopic Video Quality Assessment Metric,” Proc. of ICASSP 2017, New Orleans, LA, USA, March 2017.

8. B. Appina, K. Manasa, S. S. Channappayya, “Subjective and objective study of the relation between 3D and 2D views based on depth and bitrate,” Proc. of IS & T Electronic Imaging 2017, Burlingame, CA, USA, January 2017.

9. Sameeulla Khan Md, S. S. Channappayya, “Multiscale-SSIM Index based Stereoscopic Image Quality Assessment,” Proc. of NCC 2016 IIT Guwahati, India, March 2016.

10. S. Khan Md, S. S. Channappayya, “Sparsity Based Stereoscopic Image Quality Assessment,” Proc. of Asilomar Conference on Signals, Systems and Computers 2016, Pacific Grove, CA, USA, November 2016.

11. R. R. Tamboli, K. K. Vupparaboina, J. R. Regatti, S. Jana, S. S. Channappayya, "A Subjective Evaluation of True 3D Images,’’ Proc. of IEEE IC3D 2014, Liege, Belgium, December 2014.

HDR Image Quality Assessment

1. A. Kumar, S. Gupta, S. Chandra, S. Raman, S. S. Channappayya, “No-Reference Quality Assessment of Tone Mapped High Dynamic Range (HDR) Images Using Transfer Learning,” Proc. of QoMEX 2017, Erfurt, Germany.

2. M. Akshai Krishna, Sai Sheetal Chandra, S. S. Channappayya, S. Raman, "A Subjective and Objective Quality Assessment of Tone-Mapped Images,’’ Proc. of IEEE GlobalSIP 2015, Orlando, FL, USA, December 2015.

Multimedia Communication

In continuation with the theme of multimedia content storage and communication, we are exploring problems on the optimal allocation of network resources where the optimization criterion is the quality of experience (QoE). Specifically, our system considers models of the Long Term Evolution (LTE) network standard, and video streaming over HTTP.

Publications

1. N. Eswara, S. Chakraborty, H. P. Sethuram, K. Kuchi, A. Kumar, S. S. Channappayya, “Perceptual QoE-optimal Resource Allocation for Adaptive Video Streaming,” IEEE Transactions on Broadcasting DOI: 10.1109/TBC.2019.2954064.

2. N. Eswara, Ashique S., 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 Trans. on Circuits and Systems for Video Technology (CSVT). DOI: 10.1109/TCSVT.2019.2895223. [Abstract, Code].

3. 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,” accepted to IEEE Trans. on Circuits and Systems for Video Technology (CSVT).

4. N. Eswara, S. V. R. Dendi, S. Chakraborty, H. Sethuram, K. Kuchi, A. Kumar, S. S. Channappayya, “A Linear Regression Framework for Assessing Time-varying Subjective Quality in HTTP Streaming,” accepted to IEEE GlobalSIP 2017, Montreal, Canada, November 2017.

5. N. Eswara, S. S. Channappayya, A. Kumar, K. Kuchi, "eTVSQ based Video Rate Adaptation in Cellular Networks With α-Fair Resource Allocation, Proc. of IEEE WCNC 2016, Doha, Qatar, April 2016.

6. S. Sadhana Reddy, K. Manasa, S. S. Channappayya, "Video Packet Priority Assignment Based on Spatio-Temporal Perceptual Importance,’’ Proc. of NCC 2015, IIT Bombay.

Biomedical Imaging/Image Processing

We actively collaborate with LV Prasad Eye Institute (LVPEI) on a variety of problems related to the automated analysis of choroidal images. The primary goal of this research is to assist physicians and experts in speeding up the diagnostic process and to make suggestions on potential disease conditions.

We are working on photoacoustic imaging (both theory and system design) aimed at phantom imaging. Specifically, we are working on improved reconstruction algorithms and building a working prototype.

As part of our Grand Challenges Canada award (PI Dr. Siva Vanjari), we worked on building low-cost mobile based solutions for the quantification of thyroid stimulating hormone (TSH) concentration from the output of fabric-based test kits.

Publications

1. K. J. Francis, B. Chinni, S. S. Channappayya, R. Pachamuthu, V. S. Dogra, N. Rao, “Characterization of Lens Based Photoacoustic Imaging System,” Photoacoustics DOI: 10.1016/j.pacs.2019.01.002.

2. J. Chhablani, S. S. Channappayya, A. Richhariya, “Can an automated algorithm identify choriocapillaris in 2D-optical coherence tomography images?,” Expert Review on Ophthalmology, DOI 10.1586/17469899.2014.922875.

3. K. J. Francis, P. Mishra, P. Rajalakshmi, A. Richhariya, S. S. Channappayya, “A Simple and Accurate Matrix for Model Based Photoacoustic Imaging,” Proc. of IEEE Healthcom 2016, Munich, Germany, September 2016.

4. K. J. Francis, P. Rajalakshmi, S. S. Channappayya, "Distributed Compressed Sensing for Photo-Acoustic Imaging,’’ Proc. of IEEE ICIP 2015, Quebec City, Canada, September 2015.

5. K. J. Francis, S. S. Channappayya, P. Rajalakshmi, "Wavelet Domain Frequency interpolation for Photo-acoustic Tomography,’’ Proc. of IEEE MedCom 2014, Noida, India, November 2014.

6. K. Abhishek, M. Haloi, S. S. Channappayya, S. Vanjari, D. Dendukuri, S. Swathy, T. Choudhary, P. Bhandari, "An Enhanced Algorithm for the Quantification of Human Chorionic Gonadotropin (hCG) Level in Commercially Available Home Pregnancy Test Kits,’’ Proc. of NCC 2014, IIT Kanpur, India, February 2014.

7. S. Kakileti, S. S. Channappayya, A. Richhariya, J. Chhablani, "An Automated Algorithm for the Identification of Choriocapillaris in 2D-OCT Images,’’ Proc. of SPIE Medical Imaging 2014, San Diego, CA, USA, February 2014.

8. K. Manasa, K. V. S. N. L. Manasa Priya, S. Sadhana Reddy, S. S. Channappayya, S. Vanjari, D. Dendukuri, S. Swathy, T. Choudhary, P. Bhandari, “An Automated Algorithm for the Quantification of hCG Level in Novel Fabric-based Home Pregnancy Test Kits,” Proc. of IEEE Asilomar Conference on Signals, Systems and Computers 2013, Pacific Grove, CA USA, November 2013.

9. N. R. Mahajan, R. C. Reddy Donapati, S. S. Channappayya, S. Vanjari, A. Richhariya, J. Chhablani, “An Automated Algorithm for Blood Vessel Count and Area Measurement in 2-D Choroidal Scan Images,” Proc. of IEEE EMBC 2013, Osaka, Japan, July 2013.

10. K. T. Javvadi Appanacharya, A. K. Tatinati, H. K. Kunderu, S. K. Mohammad, S. S. Channappayya, A. Acharyya, S. Tripathi, “A Low-cost Scalable Solution for Digitizing Analog X-rays with Applications to Rural Healthcare,” Proc. of IEEE EMBC 2013, Osaka, Japan, July 2013.