Enhanced holoentropy-based encoding via whale optimization for highly efficient video coding
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ORIGINAL ARTICLE
Enhanced holoentropy-based encoding via whale optimization for highly efficient video coding Venkatesh Munagala1 · Satya Prasad Kodati1 Accepted: 8 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract High-efficiency video coding (HEVC), a video compression method is considered as the most capable descendant of the extensively deployed advanced VC (AVC). Compared with AVC, HEVC provides about twice the data compression ratio at the similar video quality level or considerably enhanced video quality at an equal bit rate. This paper proposes a novel enhanced holoentropy model for proficient systems for distributed VC (DVC). HEVC standard is considered as an archetypal system. The main contribution of this paper is the accomplishment of the encoding process in the HEVC system by enhanced holoentropy, which is linked with the proposed weighting tansig function. It necessitates considerable development when handling video sequences with high resolution. The pixel deviations under altering frames are grouped based on interest, and the outliers are eliminated with the aid of an enhanced entropy standard known as enhanced holoentropy. Here, the weight of tansig function is optimally tuned by whale optimization algorithm. To next of implementation, the suggested encoding scheme is compared with the conventional schemes concerning the number of compressed bits and computational time. By carrying out the encoding process, it reduces the video size with perceptually improved video quality or PSNR. Keywords HEVC · Holoentropy · Weighting tansig function · PSNR · Whale optimization
Abbreviations ABC AMVP AVC BE CABAC CB CM CTB CTU CU CPMV DSCNN DVC FF
B
Artificial bee colony Advanced MV prediction Advanced VC Blind extraction Context-adaptive binary arithmetic coding Coding blocks Context modeling Chroma tree block Coding tree unit Coding unit Control point motion vectors Decoder-side scalable convolutional neural network Distributed VC Fire fly
Venkatesh Munagala [email protected] Satya Prasad Kodati [email protected]
1
JNTUK, Kakinada, Andhra Pradesh, India
GA HEVC IPL IRAP JCT-VC JND LC LDP MB MVD MPEG MPRGAN MCMC MC MV NN NTC PAMC PB PSNR PSO QP
Genetic algorithm High efficiency video coding Inter-picture prediction loop Intra-random access point Joint collaborative team on video coding Just noticeable distortion Luma coding Low delay power Macro block Multi-view video plus depth Moving picture experts group Multi-level progressive refinement network via an adversarial training approach Markov Chain Monte Carlo Motion compensation Motion vector Neural network Nonzero transform coefficient Perspective affine motion compensation Prediction blocks Peak signal-to-noise ratio Particle swarm optimization Quantization parameter
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V. Munagala, K. S. Prasad
RMSE UQI VIF PU RD RA SAO SSIM SVR TB TU VBR VCS VVC VCEG WOA 3D-HEVC
Root mean squared error Universal quality image index Visual information fidelity Prediction units Rate distortion Random acces
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