Abstract
Introduction: Video Quality Assessment (VQA) is one of the important areas of study in this modern era, where video is a crucial component of communication with applications in every field. Rapid technology developments in mobile technology enabled anyone to create videos resulting in a varied range of video quality scenarios. Objectives: Though VQA was present for some time with the classical metrices like SSIM and PSNR, the advent of machine learning has brought in new techniques of VQAs which are built upon Convolutional Neural Networks (CNNs) or Deep Neural Networks (DNNs). Methods: Over the past years various research studies such as the BVQA which performed video quality assessment of nature-based videos using DNNs exposed the powerful capabilities of machine learning algorithms. BVQA using DNNs explored human visual system effects such as content dependency and time-related factors normally known as temporal effects. Results: This study explores the sharpness effect on models like BVQA. Sharpness is the measure of the clarity and details of the video image. Sharpness typically involves analyzing the edges and contrast of the image to determine the overall level of detail and sharpness. Conclusion: This study uses the existing video quality databases such as CVD2014. A comparative study of the various machine learning parameters such as SRCC and PLCC during the training and testing are presented along with the conclusion.
Abstract (translated)
引言：视频质量评估（VQA）是现代社会的一个重要研究领域，视频作为各种应用中的关键组件，已经成为人们交流的不可或缺的一部分。移动技术的快速发展使得任何人都可以创建各种视频，从而形成了一系列丰富的视频质量场景。 目标：尽管在经典矩阵如SSIM和PSNR中已经存在了一定程度的VQA，但机器学习的出现带来了新的VQA技术，这些技术基于卷积神经网络（CNN）或深度神经网络（DNN）构建。 方法：在过去的几年里，有许多研究，如使用DNN进行自然视频质量评估的BVQA，探索了机器学习算法在VQA中的强大功能。BVQA使用DNN探讨了人类视觉系统的影响，这些影响通常被称为时间因素，例如内容相关和时间因素。 结果：本研究探讨了像BVQA这样模型的 sharpness 效果。Sharpness是视频图像清晰度和细节的度量。通常通过分析图像的边缘和对比度来确定整体细节和清晰度水平。 结论：本研究使用了现有的视频质量数据库，如CVD2014。在训练和测试期间，对各种机器学习参数如SRCC和PLCC进行了比较研究，并得出了结论。
URL
https://arxiv.org/abs/2404.05764
