Bharathi Gururaj

Work place: Department of Electronics and Communication Engineering, K S Institute of Technology, Bengaluru, India

E-mail: bharathigururaj@ksit.edu.in

Website: https://orcid.org/0000-0003-2334-5864

Research Interests:

Biography

Bharathi Gururaj received her Bachelor’s degree from the PES Institute of Technology, Bangalore University, M. Tech from M S Ramaiah Institute of Technology, Bangalore, and Ph.D. degree from the VTU, Belagavi. She was employed as Assistant Professor in SVCE, Bengaluru. She also worked as an Associate Professor and Head of Department of Electronics and Communication Engineering at ACS College of Engineering, Bengaluru. She is currently working as Associate Professor at Department of Electronics and Communication Engineering, K S Institute of Technology, Bengaluru. Her research interests are in the Image and Video Processing, Wireless Communication System. She has contributed in more than 10 research journals. Her research papers were published extensively in leading international journals and conferences. She has reviewed around 50+ papers for various Journals, IEEE Conferences and International Journals. She is an Editorial Board member and Reviewer for Science Publishing Group Journal.

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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