Knowledge Engineering and Data Science
Vol 6, No 1 (2023)

Inter-Frame Video Compression based on Adaptive Fuzzy Inference System Compression of Multiple Frame Characteristics

Arief Bramanto Wicaksono Putra (Politeknik Negeri Samarinda)
Rheo Malani (Politeknik Negeri Samarinda)
Bedi Suprapty (Politeknik Negeri Samarinda)
Achmad Fanany Onnilita Gaffar (Politeknik Negeri Samarinda)
Roman Voliansky (Igor Sikorsky Kyiv Polytechnic Institute)



Article Info

Publish Date
30 May 2023

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%).

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Journal Info

Abbrev

keds

Publisher

Subject

Computer Science & IT Engineering

Description

KEDS, brings together researchers, industry practitioners, and potential users, to promote collaborations, exchange ideas and practices, discuss new opportunities, and investigate analytics frameworks on data-driven and knowledge base ...