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All Journal IAES International Journal of Robotics and Automation (IJRA) Jurnal Informatika POSITIF Knowledge Engineering and Data Science Jusikom : Jurnal Sistem Komputer Musirawas Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) J-Icon : Jurnal Komputer dan Informatika
Suprapty, Bedi
Politeknik Negeri Samarinda
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : Knowledge Engineering and Data Science

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Inter-Frame Video Compression based on Adaptive Fuzzy Inference System Compression of Multiple Frame Characteristics Arief Bramanto Wicaksono Putra; Rheo Malani; Bedi Suprapty; Achmad Fanany Onnilita Gaffar; Roman Voliansky
Knowledge Engineering and Data Science Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i12023p1-14

Abstract

Video compression is used for storage or bandwidth efficiency in clip video information. Video compression involves encoders and decoders. Video compression uses intra-frame, inter-frame, and block-based methods.  Video compression compresses nearby frame pairs into one compressed frame using inter-frame compression. This study defines odd and even neighboring frame pairings. Motion estimation, compensation, and frame difference underpin video compression methods. In this study, adaptive FIS (Fuzzy Inference System) compresses and decompresses each odd-even frame pair. First, adaptive FIS trained on all feature pairings of each odd-even frame pair. Video compression-decompression uses the taught adaptive FIS as a codec. The features utilized are "mean", "std (standard deviation)", "mad (mean absolute deviation)", and "mean (std)". This study uses all video frames' average DCT (Discrete Cosine Transform) components as a quality parameter. The adaptive FIS training feature and amount of odd-even frame pairings affect compression ratio variation. The proposed approach achieves CR=25.39% and P=80.13%. "Mean" performs best overall (P=87.15%). "Mean (mad)" has the best compression ratio (CR=24.68%) for storage efficiency. The "std" feature compresses the video without decompression since it has the lowest quality change (Q_dct=10.39%).
Co-Authors Abdul Najib Achmad Fanany Onnilita Gaffar Aldy Gustiannur Rachmat Fariyanti Fariyanti Lia Dwi Utami Malani, Rheo Muhammad Taufiq Sumadi Mulyanto Mulyanto Roman Voliansky Syamsul Arifin Utami, Lia Dwi Wikarno, Wikarno