scholarly journals Reduced reference image and video quality assessments: review of methods

2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
Shahi Dost ◽  
Faryal Saud ◽  
Maham Shabbir ◽  
Muhammad Gufran Khan ◽  
Muhammad Shahid ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Video Quality ◽  
Research Work ◽  
Research Area ◽  
Perceptual Quality ◽  
Reference Image ◽  
Video Quality Assessment ◽  
Original Image ◽  
Quality Assessments ◽  
2D And 3D

AbstractWith the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications.

No Reference Video Quality Assessment with authentic distortions using 3-D Deep Convolutional Neural Network

2020 ◽  
Vol 2020 (9) ◽  
pp. 168-1-168-7
Author(s):  
Roger Gomez Nieto ◽  
Hernan Dario Benitez Restrepo ◽  
Roger Figueroa Quintero ◽  
Alan Bovik
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Pearson Correlation ◽  
Video Quality ◽  
Perceptual Quality ◽  
Support Vector ◽  
Quality Prediction ◽  
Video Quality Assessment ◽  
Different Color

Video Quality Assessment (VQA) is an essential topic in several industries ranging from video streaming to camera manufacturing. In this paper, we present a novel method for No-Reference VQA. This framework is fast and does not require the extraction of hand-crafted features. We extracted convolutional features of 3-D C3D Convolutional Neural Network and feed one trained Support Vector Regressor to obtain a VQA score. We did certain transformations to different color spaces to generate better discriminant deep features. We extracted features from several layers, with and without overlap, finding the best configuration to improve the VQA score. We tested the proposed approach in LIVE-Qualcomm dataset. We extensively evaluated the perceptual quality prediction model, obtaining one final Pearson correlation of 0:7749±0:0884 with Mean Opinion Scores, and showed that it can achieve good video quality prediction, outperforming other state-of-the-art VQA leading models.

scholarly journals No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2768
Author(s):  
Domonkos Varga
Keyword(s):  
Quality Assessment ◽  
Video Quality ◽  
Perceptual Quality ◽  
Benford’S Law ◽  
Original Video ◽  
Video Quality Assessment ◽  
Volume Data ◽  
Digital Videos ◽  
Benford's Law

No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases.

scholarly journals RAVA: Region-Based Average Video Quality Assessment

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5489
Author(s):  
Xuanyi Wu ◽  
Irene Cheng ◽  
Zhenkun Zhou ◽  
Anup Basu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Video Quality ◽  
Video Clip ◽  
Perceptual Quality ◽  
Human Observer ◽  
Video Quality Assessment ◽  
Compressed Video

Video has become the most popular medium of communication over the past decade, with nearly 90 percent of the bandwidth on the Internet being used for video transmission. Thus, evaluating the quality of an acquired or compressed video has become increasingly important. The goal of video quality assessment (VQA) is to measure the quality of a video clip as perceived by a human observer. Since manually rating every video clip to evaluate quality is infeasible, researchers have attempted to develop various quantitative metrics that estimate the perceptual quality of video. In this paper, we propose a new region-based average video quality assessment (RAVA) technique extending image quality assessment (IQA) metrics. In our experiments, we extend two full-reference (FR) image quality metrics to measure the feasibility of the proposed RAVA technique. Results on three different datasets show that our RAVA method is practical in predicting objective video scores.

A 3D Video Quality Assessment Method Based on the Disparity Map

2013 ◽  
Vol 791-793 ◽  
pp. 1061-1063
Author(s):  
Tong Zhou
Keyword(s):  
Quality Assessment ◽  
Hot Spot ◽  
Video Quality ◽  
Research Work ◽  
Quality Measures ◽  
Assessment Method ◽  
3D Video ◽  
Video Quality Assessment ◽  
Disparity Map ◽  
Quality Assessment Method

3D video quality assessment is a hot spot in the market of TV industry. Most Research work are based on the DIBR. However in TV industry the main form of 3DTV is side by side.Up to now there have been very few video quality measures to deal with this situation.This paper proposes a 3D video quality method based on the disparity map to deal with this situation.The method comes out with the broadcast of CCTV3D on Jan 1st 2012.

scholarly journals Hierarchical Gradient Similarity Based Video Quality Assessment Metric

2017 ◽  
Author(s):  
Jie Yang ◽  
Jian Xiong ◽  
Guan Gui ◽  
Rongfang Song ◽  
Wang Luo ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Visual Information ◽  
Quality Evaluation ◽  
State Of The Art ◽  
Video Quality ◽  
Human Perception ◽  
Perceptual Quality ◽  
Video Quality Assessment ◽  
Visual Areas ◽  
Primate Visual Cortex

Video quality assessment (VQA) plays an important role in video applications for quality evaluation and resource allocation. It aims to evaluate the video quality consistent with the human perception. In this letter, a hierarchical gradient similarity based VQA metric is proposed inspired by the structure of the primate visual cortex, in which visual information is processed through sequential visual areas. These areas are modeled with the corresponding measures to evaluate the overall perceptual quality. Experimental results on the LIVE database show that the proposed VQA metric significantly outperforms the state-of-the-art VQA metrics.

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