Open Access

ARTICLE

High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection

Krishan Kumar^1,*, Mohamed Abouhawwash^2,3, Amit Kant Pandit¹, Shubham Mahajan¹, Mofreh A. Hogo⁴

1 School of Electronics & Communication Engineering, Shri Mata Vaishno Devi University, Katra, 182320, India
2 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
3 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, 48824, MI, USA
4 Electrical Engineering Department, Faculty of Engineering, Benha University, Benha, 13518, Egypt

* Corresponding Author: Krishan Kumar. Email:

Computers, Materials & Continua 2022, 73(1), 1977-1993. https://doi.org/10.32604/cmc.2022.027850

Received 27 January 2022; Accepted 08 March 2022; Issue published 18 May 2022

Abstract

The high-efficiency video coder (HEVC) is one of the most advanced techniques used in growing real-time multimedia applications today. However, they require large bandwidth for transmission through bandwidth, and bandwidth varies with different video sequences/formats. This paper proposes an adaptive information-based variable quantization matrix (AI-VQM) developed for different video formats having variable energy levels. The quantization method is adapted based on video sequence using statistical analysis, improving bit budget, quality and complexity reduction. Further, to have precise control over bit rate and quality, a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths. The same should be handy to self-adapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio (PSNR), bit-budget and time complexity for different videos a noticeable improvement in rate-distortion (RD) performance is achieved. Using the proposed AI-VQM technique, more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method (VQM) algorithm with approximately a rise of 10%–20% based on different video sequences/formats

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Keywords

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