International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
A survey on Measurement of Objective Video Quality in Social Cloud using Machine Learning Dhanashri Adhav1, Abhijit Shinde 2, Nana Modhale 3, Shreyas Sukale 4, Prof. Manisha Darak5 5Professor, Computer Engineering, RMD Sinhgad School of Engineering, Warje, India 1, 2,3,4 Students Dept. of Computer Engineering, RMD Sinhgad School of Engineering, Warje, India
---------------------------------------------------------------------***--------------------------------------------------------------------and robust quality predictions even in complex video Abstract - Assessing objective video quality is critical in
environments. In social cloud applications, machine learning-based video quality assessment is particularly useful to optimize video transmission and distribution processes. By predicting the objective quality of video content, machine learning algorithms can optimize video bit rate, resolution, and other parameters to provide the best user experience while minimizing bandwidth usage. This paper aims to provide a comprehensive survey of the stateof-the-art in machine learning-based objective video quality measurement in the context of social cloud applications. An examination of recent studies that have used machine learning algorithms to evaluate video quality and compare the performance of different techniques in terms of precision, efficiency and scalability. The benefits and limitations of measuring video quality based on machine learning and identifying potential leads for future research. Overall, this article provides valuable insights into the state of the art for measuring video quality objectively and offers practical tips for improving video quality in social cloud applications using machine learning techniques.
applications such as video streaming, video conferencing, and video surveillance. Although traditional subjective assessment methods are commonly used, they can be time-consuming and expensive. Objective methods for assessing video quality have been proposed, but often lack accuracy and consistency. In recent years, machine learning techniques, particularly deep learning models such as convolutional neural networks (CNNs), have shown promise for objective assessments of video quality. This study proposes a new method that CNN uses to understand the relationship between video characteristics and subjective quality judgments. The proposed method uses a large collection of video data evaluated by human observers to train CNN. The videos were then processed to extract qualityrelated features, which were used to train a machine learning model to predict objective quality outcomes. The model was tested on a separate set of videos, and the results showed that the proposed method achieved a high level of accuracy in predicting high quality objective outcomes. The results show that the proposed method achieves high accuracy and consistency in predicting subjective qualitative outcomes. Machine learning techniques can provide an objective assessment of video quality, which could benefit content creators, consumers, and service providers.
2. TERMONOLOGIES 2.1 Video Quality Assessment
Key Words: Social Cloud, Quality of Experience (QoE), Convolutional Neural Network (CNN), Deep Learning, Video Quality Assessment, Peak Signal-to-Noise Ratio (PSNR)
Video Quality Assessment (VQA) is a critical aspect of measuring the quality of videos. It involves the use of various techniques and algorithms to evaluate the perceptual quality of a video, based on various visual features such as sharpness, color accuracy, and contrast. VQA can be performed using subjective or objective methods. In subjective methods, human raters provide quality scores based on their perception, while objective methods use machine learning algorithms to estimate quality based on various metrics. VQA is important for video compression, streaming, and other applications where video quality is a critical factor.
1. INTRODUCTION The rapid advances in video technology and widespread availability of cloud-based social media platforms have led to a growing demand for the delivery of high-quality video content. Video content quality has a significant impact on user experience, and objective measurement of video quality is essential to ensure optimal user experience. Machine learning techniques are widely used to develop objective video quality measurement models that can automatically predict video quality based on various visual and perceptual characteristics. This approach has Several advantages over traditional metrics based on simplified measures such as pixel distortion or image Similarity. Machine learning algorithms can learn the complex relationship between video properties and perceptual quality, enabling accurate
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2.2 Deep Learning Deep learning is a powerful technique for measuring video quality, especially for objective evaluation. Deep learning algorithms like convolutional neural networks (CNNs) can extract visual features from videos that are difficult to quantify using traditional methods. These functions can be used to train models to predict video quality metrics such as
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