Tree Social Relations Optimization-Based ReLU-BiLSTM Framework for Improving Video Quality in Video Compression

Abstract

High-Efficiency Video Coding (HEVC) has a higher coding efficiency, its encoding performance must be increased to keep up with the expanding number of multimedia applications. Therefore, this paper proposes a novel Rectified Linear Unit-Bidirectional Long Short-Term Memory-based Tree Social Relations Optimization (ReLU-BiLSTM-based TSRO) method to enhance the quality of video transmission. The significant objective of our proposed method aims in enhancing the standards of entropy encoding process in HEVC. Here, context-adaptive binary arithmetic coding (CABAC) framework which is prevalent and an improved form of entropy coding model is utilized in HEVC standards. In addition to this, the performances of the proposed method are determined by evaluating various measures such as mean square error, cumulative distribution factor, compression ratio, peak signal-to-noise ratio (PSNR) and bit error rate. Finally, the proposed method is examined with five different sequences of video from football, tennis, garden, mobile and coastguard. The performances of the proposed method are compared with various approaches, and the result analysis shows that the proposed method attained minimum mean square error (MSE) loss with maximum PSNR rate.