Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/52546
Title: Fast Intermode Selection for HEVC Video Coding Using Phase Correlation
Contributor(s): Podder, Pallab Kanti (author); Paul, Manoranjan (author); Murshed, Manzur (author); Chakraborty, Subrata  (author)orcid 
Publication Date: 2015-01-15
DOI: 10.1109/DICTA.2014.7008109
Handle Link: https://hdl.handle.net/1959.11/52546
Abstract: 

The recent High Efficiency Video Coding (HEVC) Standard demonstrates higher rate-distortion (RD) performance compared to its predecessor H.264/AVC using different new tools especially larger and asymmetric inter-mode variable size motion estimation and compensation. This requires more than 4 times computational time compared to H.264/AVC. As a result it has always been a big concern for the researchers to reduce the amount of time while maintaining the standard quality of the video. The reduction of computational time by smart selection of the appropriate modes in HEVC is our motivation. To accomplish this task in this paper, we use phase correlation to approximate the motion information between current and reference blocks by comparing with a number of different binary pattern templates and then select a subset of motion estimation modes without exhaustively exploring all possible modes. The experimental results exhibit that the proposed HEVC-PC (HEVC with Phase Correlation) scheme outperforms the standard HEVC scheme in terms of computational time while preserving-the same quality of the video sequences. More specifically, around 40% encoding time is reduced compared to the exhaustive mode selection in HEVC.

Publication Type: Conference Publication
Conference Details: DICTA 2014: 2014 International Conference on Digital Image Computing: Techniques and Applications, Wollongong, Australia, 25th - 27th November, 2014
Source of Publication: Proceedings of the IEEE International Conference on Digital Image Computing: Techniques and Applications, p. 1-8
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Place of Publication: United States of America
Fields of Research (FoR) 2020: 460199 Applied computing not elsewhere classified
460305 Image and video coding
461199 Machine learning not elsewhere classified
Socio-Economic Objective (SEO) 2020: 280115 Expanding knowledge in the information and computing sciences
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Appears in Collections:Conference Publication
School of Science and Technology

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