On the Performance of Video Quality Assessment Metrics under Different Compression and Packet Loss Scenarios

Author:

Martínez-Rach Miguel O.1,Piñol Pablo1,López Otoniel M.1,Perez Malumbres Manuel1,Oliver José2,Calafate Carlos Tavares2

Affiliation:

1. Department of Physics and Computer Engineering, the Miguel Hernández University, Avenida de Universidad s/n, Elche, 03202 Alicante, Spain

2. Department of Computer Engineering, Polytechnic University of Valencia, Camino de Vera s/n, Building G1, 46022 Valencia, Spain

Abstract

When comparing the performance of video coding approaches, evaluating different commercial video encoders, or measuring the perceived video quality in a wireless environment, Rate/distortion analysis is commonly used, where distortion is usually measured in terms of PSNR values. However, PSNR does not always capture the distortion perceived by a human being. As a consequence, significant efforts have focused on defining an objective video quality metric that is able to assess quality in the same way as a human does. We perform a study of some available objective quality assessment metrics in order to evaluate their behavior in two different scenarios. First, we deal with video sequences compressed by different encoders at different bitrates in order to properly measure the video quality degradation associated with the encoding system. In addition, we evaluate the behavior of the quality metrics when measuring video distortions produced by packet losses in mobile ad hoc network scenarios with variable degrees of network congestion and node mobility. Our purpose is to determine if the analyzed metrics can replace the PSNR while comparing, designing, and evaluating video codec proposals, and, in particular, under video delivery scenarios characterized by bursty and frequent packet losses, such as wireless multihop environments.

Funder

Spanish Ministry of Education and Science

Publisher

Hindawi Limited

Subject

General Environmental Science,General Biochemistry, Genetics and Molecular Biology,General Medicine

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine Learning approach for global no-reference video quality model generation;Applications of Digital Image Processing XLI;2018-09-17

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