期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：1
DOI：10.14569/IJACSA.2022.0130168
语种：English
出版社：Science and Information Society (SAI)
摘要：High efficiency video (HEVC) coding made its mark as a codec which compress with low bit rate than its preceding codec that is H.264, but the factor that stop HEVC from many applications is its complex encoding procedure. The rate distortion optimisation (RDO) cost calculation in HEVC consume complex calculations. In this paper, we propose a method to cross out the issue of complex calculations by replacing the traditional inter-prediction procedure of brute force search for RDO by a deep convolutional neural network to predict and perform this process. In the first step, the modelling of the deep depth decision algorithm is done with optimum specifications using convolutional neural network (CNN). In the next step, the model is designed and trained with dataset and validated. The trained model is tested by pipelining it to the original HEVC encoder to check its performance. We also evaluate the efficiency of the model by comparing the average time of encoding for various resolution video input. The testing is done with mutually independent input to maintain the accuracy of the system. The system shows a substantial saving in encoding time that proves the complexity reduction in HEVC.
关键词：CNN; HEVC; deep learning; RDO; encoding time; complexity reduction
