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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Periapandi, Hosanna Princye
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Optimal coding unit decision for early termination in high efficiency video coding using enhanced whale optimization algorithm Krishnegowda, Suhas Shankarnahalli; Periapandi, Hosanna Princye
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6378-6387

Abstract

Video compression is an emerging research topic in the field of block based video encoders. Due to the growth of video coding technologies, high efficiency video coding (HEVC) delivers superior coding performance. With the increased encoding complexity, the HEVC enhances the rate-distortion (RD) performance. In the video compression, the out-sized coding units (CUs) have higher encoding complexity. Therefore, the computational encoding cost and complexity remain vital concerns, which need to be considered as an optimization task. In this manuscript, an enhanced whale optimization algorithm (EWOA) is implemented to reduce the computational time and complexity of the HEVC. In the EWOA, a cosine function is incorporated with the controlling parameter A and two correlation factors are included in the WOA for controlling the position of whales and regulating the movement of search mechanism during the optimization and search processes. The bit streams in the Luma-coding tree block are selected using EWOA that defines the CU neighbors and is used in the HEVC. The results indicate that the EWOA achieves best bit rate (BR), time saving, and peak signal to noise ratio (PSNR). The EWOA showed 0.006-0.012 dB higher PSNR than the existing models in the real-time videos.
Scaler enhanced deformable attention with graph neural network for video compression Kasinathaperumal, Revathi; Periapandi, Hosanna Princye
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1473-1485

Abstract

Video compression is widely used to reduce bandwidth and storage requirements when storing and transmitting videos, most existing neural video compression approaches adopt the predictive residue-coding framework, which is suboptimal for removing redundancy across frames. Additionally, minimizing only the pixel-wise differences between the raw and decompressed frames is ineffective in improving the perceptual quality of the videos, blocking artifacts degrade the visual quality, especially near edges and texture areas. Hence, to solve these problems, this research proposes a scaler enhanced deformable attention graph neural network (SEDA-GNN) to utilized for reduce inter-frame redundancy by employing a deformable attention mechanism that efficiently captures motion and structural changes, thereby minimizing redundancy. Modelling complex temporal dynamics with graph neural networks (GNNs) captures dependencies between frames, thereby facilitating highly efficient video encoding, then constrained directional enhancement filter (CDEF) effectively reduces blocking artifacts while preserving sharp edges through directional and constrained filtering, thereby improving visual quality in compressed video. The SEDA-GNN approach achieved a bjontegaard delta bit rate (BD-BR) reduction of 2.372% on the joint collaborative team on video coding (JCT-VC) database and 3.230% of BD-BR on the ultra video group (UVG) dataset, demonstrating significant performance when compared to invertible neural networks (INNs).
Co-Authors Kasinathaperumal, Revathi Krishnegowda, Suhas Shankarnahalli