Adaptive motion estimation algorithm and hardware designs for H.264 multiview video coding

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Erdayandı, Kamil (2014) Adaptive motion estimation algorithm and hardware designs for H.264 multiview video coding. [Thesis]

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Abstract

Multiview Video Coding (MVC) is the process of efficiently compressing stereo (2 views) or multiview video signals. The improved compression efficiency achieved by H.264 MVC comes with a significant increase in computational complexity. Therefore, in this thesis, we propose novel techniques for significantly reducing the amount of computations performed by full search motion estimation algorithm for H.264 MVC, and therefore significantly reducing the energy consumption of full search motion estimation hardware for H.264 MVC with very small PSNR loss and bitrate increase. We also propose an adaptive fast motion estimation algorithm for reducing the amount of computations performed by H.264 MVC motion estimation, and therefore reducing the energy consumption of H.264 MVC motion estimation hardware even more with additional very small PSNR loss and bitrate increase. We also propose an adaptive H.264 MVC motion estimation hardware for implementing the proposed adaptive fast motion estimation algorithm. The proposed motion estimation hardware is implemented in Verilog HDL and mapped to a Xilinx Virtex-6 FPGA. The proposed motion estimation hardware has less energy consumption than the full search motion estimation hardware for H.264 MVC and the full search motion estimation hardware for H.264 MVC including the proposed computation reduction techniques.
Item Type: Thesis
Uncontrolled Keywords: H.264. -- Multiview video coding. -- Motion estimation. -- Hardware design. -- Çok bakışlı video kodlama. -- Hareket tahmini. -- Donanım tasarımı.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: IC-Cataloging
Date Deposited: 29 Mar 2018 11:20
Last Modified: 26 Apr 2022 10:14
URI: https://research.sabanciuniv.edu/id/eprint/34336

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