Mathivanan Murugavelu

Work place: Department of Electronics and Communication Engineering, Salem College of Engineering and Technology, Salem, India

E-mail: mathivananacs@gmail.com

Website: https://orcid.org/0000-0002-1323-2412

Research Interests:

Biography

Mathivanan Murugavelu obtained his B.E degree in ECE from University of Madras (2001), M.E degree in Applied Electronics (2007) and Ph.D in ECE from Anna University, Chennai (2014). He has more than 20 years of teaching experience and more than 10 years of research experience. He has published more than 30 research articles including SCI, Scopus indexed International Journals, Book chapters and National & International Conferences. His area of interest includes Signal Processing, Speech & Image Processing, Networking and IoT. Presently he is working as Professor, Department of ECE, Salem College of Engineering & Technology, Salem, Tamilnadu affiliated to Anna University. He has reviewed many research papers in various Journals and Conferences. He has acted as Indian Examiner for the research scholars of few Universities. He is the life time member of ISTE and IETE professional societies. He is guiding the research for 4 candidates and 2 of them completed their Ph.D under VTU, Karnataka.

Author Articles
Enhancing In-loop Filter of HEVC with Integrated Residual Encoder-Decoder Network and Convolutional Neural Network

By Vanishree Moji Bharathi Gururaj Mathivanan Murugavelu

DOI: https://doi.org/10.5815/ijigsp.2025.04.04, Pub. Date: 8 Aug. 2025

High Efficiency Video Coding (HEVC) often known as H.265 is a video compression method that outperforms its predecessor H.264. In HEVC, an in-loop filter is an additional processing step that removes compressing artifacts from decoding video frames while improving visual quality. This research article proposes an improved in-loop filter that incorporates a Residual Encoder-Decoder Network based Deblocking Filter (REDNetDF) and a Convolutional Neural Network based Sample Adaptive Offset (CNN-SAO) filter, which together eliminates the smallest range of artifacts in compression video frames. The quantization frame is subjected to REDNetDF, which removes a minute number of blocking artifacts from the compressed frame. To eliminate the ringing artifacts in the compressed frame, CNN-SAO filter is used. The proposed method is used to evaluate the publicly available UVG dataset. To demonstrate efficiency, the new model is evaluated using a variety of metrics. The outcome of this study provides better results like PSNR of 49.7 dB and the SSIM of 0.97 in comparison with other techniques. Besides, the model's outcome indicates an MSE of 1.8 and saves 24.9% more bits on average to provide the same level of quality as previous techniques. The proposed framework also suppresses time complexities regarding encoding and decoding times with the results of 90.5 and 4.5 seconds on average correspondingly.

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