A New Blind Video Quality Metric for Assessing Different Turbulence Mitigation Algorithms

Abstract

Although many algorithms have been proposed to mitigate air turbulence in optical videos, there do not seem to be consistent blind video quality assessment metrics that can reliably assess different approaches. Blind video quality assessment metrics are necessary because many videos containing air turbulence do not have ground truth. In this paper, a simple and intuitive blind video quality assessment metric is proposed. This metric can reliably and consistently assess various turbulent mitigation algorithms for optical videos. Experimental results using more than 10 videos in the literature show that the proposed metrics correlate well with human subjective evaluations. Compared with an existing blind video metric and two other blind image quality metrics, the proposed metrics performed consistently better.

DCT Domain Detail Image Enhancement for More Resolved Images

“…In objective quality evaluation, there are image signal feature-based and learningbased methods [22][23][24][25]. As an image signal feature-based method, the cumulative probability of blur detection (CPBD) metric is adopted, which has been reported to produce a more stable image quality score [22].…”

Blind video watermarking scheme for medical video authentication

“…However, this disfiguration is not desired in medical video watermarking, leading to misdiagnosis and directly affecting the surgeon's decision toward surgery operation. Blind watermarking [ [2] , [3] , [4] ] is a promising technology for addressing these critical issues. The typical watermarking procedures are as follows: First, the watermark is concealed in the cover media (video, image, or audio) using special embedding techniques to generate a watermarked media.…”