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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Agus Purwadi Purwadi
Politeknik Negeri Jember
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Performance Evaluation of Motion Estimation and Compensation Algorithms in SNR Scalable Video Encoding Agus Purwadi Purwadi; Hendra Yufit Riskiawan; Agus Hariyanto; Nugroho Setyo Wibowo; Rani Purbaningtyas
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2466

Abstract

Motion estimation is the sequential determination of the direction of motion of an object in a video. The movement of an object is denoted by the term motion vector. Between the current and reference frames, motion vectors can signify shift points. The SAD (Sum of Absolute Different) block matching technique is fundamentally dependent on the assessment of an object's motion. This study proposes a hybrid approach that integrates the Three-Step Search (TSS) and Full Search (FS) algorithms. This integration aims to design a block matching algorithm that is applied to video encoding using signal-to-noise ratio (SNR) scalability. From this design, the study aims to obtain the performance results and evaluate the motion estimation process using both the TSS and FS algorithms for performance comparison in SNR scalability video encoding, in terms of video frame quality, bit rate, and PSNR, based on the average comparison of the two algorithms. Based on the experimental results, the FS algorithm achieved a total BD-PSNR of 0.22 dB with an efficiency rate of 12.45%, whereas the TSS algorithm achieved a total BD-PSNR of 0.18 dB and an efficiency rate of 7.6%. Therefore, the FS algorithm demonstrates superior performance compared to the proposed TSS algorithm in video transmission with SNR scalability.
Co-Authors Agus Hariyanto Hendra Yufit Riskiawan Nugroho Setyo Wibowo Rani Purbaningtyas