Korishetti, A. C. and Malemath, V. S. (2026) Optimal block search mechanism using deep recurrent neural network for enabling the code-efficiency in hevc. Journal of Engineering, Design and Technology, 24(4), pp. 35-62. ISSN 1726-0531
Abstract
Purpose – High-efficiency video coding (HEVC) is the latest video coding standard that has better coding efficiency than the H.264/advanced video coding (AVC) standard. The purpose of this paper is to design and develop an effective block search mechanism for the video compression-HEVC standard such that the developed compression standard is applied for the communication applications. Design/methodology/approach – In the proposed method, an rate-distortion (RD) trade-off, named regressive RD trade-off is used based on the conditional autoregressive value at risk (CaViar) model. The motion estimation (ME) is based on the new block search mechanism, which is developed with the modification in the Ordered Tree-based Hex-Octagon (OrTHO)-search algorithm along with the chronological Salp swarm algorithm (SSA) based on deep recurrent neural network (deepRNN) for optimally deciding the shape of search, search length of the tree and dimension. The chronological SSA is developed by integrating the chronological concept in SSA, which is used for training the deep RNN for ME. Findings – The competing methods used for the comparative analysis of the proposed OrTHO-search based RD + chronological-salp swarm algorithm (RD + C-SSA) based deep RNN are support vector machine (SVM), fast encoding framework, wavefront-based high parallel (WHP) and OrTHO-search based RD method. The proposed video compression method obtained a maximum peak signal-to-noise ratio (PSNR) of 42.9180 dB and a maximum structural similarity index measure (SSIM) of 0.9827. Originality/value – In this research, an effective block search mechanism was developed with the modification in the OrTHO-search algorithm along with the chronological SSA based on deepRNN for the video compression-HEVC standard.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | deep learning; energy efficiency; motion estimation; optimization; video compression |
| Index terms: | methodology, distortion, compression, comparative analysis, encoding, neural network, dimension, efficiency, energy efficiency, deep learning, estimation |
| Subjects: | material properties and characteristics, performance management, data analysis and analytics, data science, artificial intelligence, health monitoring assessment and metrics, financial and cost management, sustainability and energy, networking, research methods |
| Topics: | Sustainability, Engineering Principles, Health and Safety, Quality Management, Research Practice, Construction Materials, Cost Management, Digital Applications |
| Descriptive scope: | 4 PCTA |
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