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Article

Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics

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Department of Mathematics, University of Baghdad, Baghdad, Iraq
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Computer Science Department, Liverpool John Moores University, Byrom Street, Liverpool L33AF, UK
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Computer Science Department, College of Engineering and Computer Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
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Artificial Intelligence Team, Datactics, Belfast BT1 3LG, UK
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Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE
*
Author to whom correspondence should be addressed.
Received: 5 February 2020 / Revised: 11 March 2020 / Accepted: 11 March 2020 / Published: 20 March 2020
The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, recognition and understanding and efficient processing of large amounts of video data. This research proposes a novel unified approach to lossy and lossless video frame compression, which is beneficial for the autonomous processing and enhanced representation of high-resolution video data in various domains. The proposed fast block matching motion estimation technique, namely mean predictive block matching, is based on the principle that general motion in any video frame is usually coherent. This coherent nature of the video frames dictates a high probability of a macroblock having the same direction of motion as the macroblocks surrounding it. The technique employs the partial distortion elimination algorithm to condense the exploration time, where partial summation of the matching distortion between the current macroblock and its contender ones will be used, when the matching distortion surpasses the current lowest error. Experimental results demonstrate the superiority of the proposed approach over state-of-the-art techniques, including the four step search, three step search, diamond search, and new three step search. View Full-Text
Keywords: remote sensing; IOT; smart city; block-matching algorithm; macroblocks; video compression; motion estimation remote sensing; IOT; smart city; block-matching algorithm; macroblocks; video compression; motion estimation
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MDPI and ACS Style

Ahmed, Z.; Hussain, A.J.; Khan, W.; Baker, T.; Al-Askar, H.; Lunn, J.; Al-Shabandar, R.; Al-Jumeily, D.; Liatsis, P. Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics. Remote Sens. 2020, 12, 1004. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12061004

AMA Style

Ahmed Z, Hussain AJ, Khan W, Baker T, Al-Askar H, Lunn J, Al-Shabandar R, Al-Jumeily D, Liatsis P. Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics. Remote Sensing. 2020; 12(6):1004. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12061004

Chicago/Turabian Style

Ahmed, Zayneb, Abir J. Hussain, Wasiq Khan, Thar Baker, Haya Al-Askar, Janet Lunn, Raghad Al-Shabandar, Dhiya Al-Jumeily, and Panos Liatsis. 2020. "Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics" Remote Sensing 12, no. 6: 1004. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12061004

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