With the surge in demand for high-quality video content over various
platforms, efficient video compression techniques have become
indispensable. High-Efficiency Video Coding (HEVC) has been a
cornerstone, yet further enhancements are essential for optimal
compression. Despite HEVC’s advancements, achieving optimal
compression while maintaining video quality remains challenging.
Additionally, existing methods often overlook the computational
complexity, hindering real-time applications. We propose a novel
approach integrating HEVC with Non-Linear Convolutional
MobileNet (NLCM) for enhanced compression efficiency. Our method
employs a rate-distortion optimization framework, leveraging the
capabilities of both HEVC and NLCM to achieve superior compression
performance. NLCM provides adaptive filtering, enhancing spatial and
temporal correlations, while HEVC ensures high compression
efficiency. Through experimentation on standard video datasets, our
method demonstrates significant improvements over existing
techniques. Compared to HEVC alone, our approach achieves up to
30% reduction in bitrate at equivalent perceptual quality levels.
Moreover, computational complexity is reduced by 15%, enabling real-
time applications without compromising performance. The proposed
method exhibits competitive results across various resolutions and
frame rates, making it versatile for diverse video compression
scenarios.
D. Prabakar1, K. Venkata Ramana2, A. Thangam3, S. Esakki Rajavel4, Geogen George5
Karpagam College of Engineering, India1, QIS College of Engineering and Technology, India2, Pondicherry University Community College, India3, Karpagam Academy of Higher Education, India4, University of Technology and Applied Sciences, Sultanate of Oman5
HEVC, Non-Linear Convolutional MobileNet, Compression Efficiency, Rate-Distortion Optimization
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 14 , Issue: 4 , Pages: 3273 - 3281 )
Date of Publication :
May 2024
Page Views :
202
Full Text Views :
16
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