Research @ MEPCAG

Our research revolves around multimedia processing, specifically within the realms of 2D and 3D dimensions. The primary emphasis is on addressing challenges related to multimedia storage, biomedical signals, compression distortions, statistical analysis, and psychovisual assessment. The overarching objective of this research is to create cutting-edge models for content analysis, prediction of visual comfort, and quality assessment. These models aim to monitor artifacts and distortions in multimedia content by employing perceptual-cognitive analysis, mimicking the intricacies of the human visual system.

Multimedia Quality Assessment:

The pursuit of research on multimedia quality assessment is driven by various motivations. Firstly, enhancing user experiences is crucial in today's digital age, where engaging and immersive multimedia content is highly sought after. Secondly, industries relying on multimedia, such as entertainment, marketing, and gaming, are motivated to optimize their offerings to gain a competitive edge. Thirdly, advancing communication technologies require efficient handling and delivery of multimedia data, necessitating comprehensive quality assessment techniques. Fourthly, standardization is essential to ensure consistent user experiences across different platforms and devices. Additionally, by developing objective metrics, researchers can bridge the subjectivity gap and provide quantifiable assessments of multimedia quality. Our team is actively developing subjective and objective quality assessment algorithms for both image and video multimedia content.

Research outcomes:

2D Multimedia Quality Assessment:

K. V. S. N. L. M. Priya, B.Appina, S. S. Channappayya, “No reference image quality assessment using statistics of sparse representations,” in IEEE International Conference on Signal Processing and Communications, pp. 1 − 5, June 2016.

Stereoscopic (S3D) Multimedia Quality Assessment:

S. Khan Md, B.Appina, S. S. Channappayya, "Full-reference stereo image quality assessment using natural stereo scene statistics," Signal Processing Letters, IEEE 22 (11) (2015) 1985-1989. doi:10.1109/LSP.2015. 2449878.
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.
B. Appina and S. S. Channappayya, " Full-reference 3-D video quality assessment using scene component statistical dependencies," Signal Processing Letters, IEEE 25 (6) (2018) 823-827. doi:10.1109/LSP.2018. 2829107.
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," in Transactions on Image Processing, IEEE 28 (10) (2019) 5027-5040. doi: 10.1109/TIP.2019.2914950.
S. Biswas, B. Appina, P.A. Kara, A. Simon, ''Jomodevi: A joint motion and depth visibility prediction algorithm for perceived stereoscopic 3D quality," Signal Processing: Image Communication, 108 (2022) 116820. doi: 10.1016/j.image.2022.116820.
A. K. R. Poreddy, P.A. Kara, R. R. Tamboli, A. Simon, B. Appina, ''CoDIQE3D: A completely-blind, no-reference stereoscopic image quality estimator using joint color and depth statistics," in Visual Computer, Springer, 2023, doi: 10.1007/s00371-022-02760-3.

Light Field Multimedia:

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. (*equal contribution).

Virtual Reality (VR) and Augmented Reality (AR) Multimedia Quality Assessment:

A. K. R. Poreddy, B. Appina, P. Kokil, “FFVRIQE: A Feature Fused Omnidirectional Virtual Reality Image Quality Estimator”, accepted to Transactions on Instrumentation and Measurement, IEEE, 2024.
A. K. R. Poreddy, R. B. C. Gnaneswaram, B. Appina, P. Kokil, R. B. Pachori, “No-Reference Virtual Reality Image Quality Evaluator Using Global and Local Natural Scene Statistics”, in Transactions on Instrumentation and Measurement, IEEE, vol. 72, pp. 1-16, doi: 10.1109/TIM.2023.3322995.
A. K. R. Poreddy and B.Appina, “BVRIQE: A Completely Blind No Reference Virtual Reality Image Quality Evaluator" accepted to International Conference on Signal Processing and Communications, IEEE, July 2022.

Multimedia Compression Analysis:

B. R. Konduru, V. Pudi, B. Appina, A. Chattopadhyay, “Image compression based on Near Lossless Predictive Measurement Coding for Block based Compressive sensing”, accepted to Transactions on Circuits and Systems II: Express Briefs, IEEE, 2023.
B. R. Konduru, V. Pudi, B. Appina, “Design of Low Complexity Quantized Compressive Sensing using Measurement Predictive Coding”, accepted to Transactions on Very Large Scale Integration, IEEE, 2024.

Biomedical Signal and Multimedia Processing:

Biomedical signal and multimedia processing are vital for advancing healthcare, enabling precise analysis of physiological data and enhancing medical imaging to facilitate accurate diagnostics and treatment planning in a multimedia-rich medical environment.

Laparoscopic Multimedia:

H. H. Borate, B.Appina, A. Simon, "A full-reference laparoscopic video quality assessment algorithm," in Optics and Photonics for Information Processing XV, vol. 11841, p. 118410E, International Society for Optics and Photonics, 2021.
A. K. R. Poreddy, B. V. Atmakuru, T. B. Krishna, P. Kokil, B. Appina, “Enhancing Laparoscopic Video Quality Assessment: A Model Addressing Sensor and Channel Distortions”, accepted to Sensors Letters, IEEE.

Biosensor Signal:

V. G. Yamalakonda, R. B. Pachori, B. Appina, A. K. Singh, “Embedded Cubature Kalman Filter for Glucose and Insulin Concentration Estimation Using Noisy Glucose Sensor Data and Multiple Meal Disturbances ”, accepted to Sensors Letters, IEEE, 2024.

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