Knowledge Engineering and Data Science


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

Putra, Arief Bramanto Wicaksono (Unknown)
Malani, Rheo (Unknown)
Suprapty, Bedi (Unknown)
Gaffar, Achmad Fanany Onnilita (Unknown)
Voliansky, Roman (Unknown)



Article Info

Publish Date
01 Jul 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

publication:keds

Publisher

Subject

Computer Science & IT Engineering

Description

The journal welcomes experimental and theoretical findings on data science and knowledge engineering along with their applications to real-life ...