Only-Reference Video Quality Assessment for Video Coding Using Convolutional Neural Network

2018 ◽  
Author(s):  
Khanh QuoC Dinh ◽  
Jongseok Lee ◽  
Jaehwan Kim ◽  
Youngo Park ◽  
Kwang Pyo Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Video Coding ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Video Quality ◽  
Video Quality Assessment

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 A Multi-parameter Video Quality Assessment Model Based on 3D Convolutional Neural Network on the Cloud

2021 ◽  
Vol 1 (2) ◽  
pp. 14-22
Author(s):  
Xue Li ◽  
Jiali Qiu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Packet Loss ◽  
Video Quality ◽  
Assessment Model ◽  
Video Quality Assessment ◽  
Network Bandwidth ◽  
Model Based ◽  
The Impact

As the rapid development of big data and the artificial intelligence technology, users prefer uploading more and more local files to the cloud server to reduce the pressure of local storage, but when users upload more and more duplicate files , not only wasting the network bandwidth, but also bringing much more inconvenience to the server management, especially images and videos. To solve the problems above, we design a multi-parameter video quality assessment model based on 3D convolutional neural network in the video deduplication system, we use a method similar to analytic hierarchy process to comprehensively evaluate the impact of packet loss rate, codec, frame rate, bit rate, resolution on video quality, and build a two-stream 3D convolutional neural network from the spatial flow and timing flow to capture the details of video distortion, set the coding layer to remove redundant distortion information. Finally, the LIVE and CSIQ data sets are used for experimental verification, we compare the performance of the proposed scheme with the V-BLIINDS scheme and VIDEO scheme under different packet loss rates. We also use the part of data set to simulate the interaction process between the client and the server, then test the time cost of the scheme. On the whole, the scheme proposed in this paper has a high quality assessment efficiency.

Video quality assessment using neural network based on multi-feature extraction

2003 ◽  
Author(s):  
Susu Yao ◽  
Weisi Lin ◽  
Zhongkang Lu ◽  
EePing Ong ◽  
Xiao K. Yang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Quality Assessment ◽  
Video Quality ◽  
Video Quality Assessment

scholarly journals VVC In-Loop Filtering Based on Deep Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Soulef Bouaafia ◽  
Seifeddine Messaoud ◽  
Randa Khemiri ◽  
Fatma Elzahra Sayadi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Video Coding ◽  
Convolutional Neural Network ◽  
Video Streaming ◽  
High Efficiency ◽  
Dynamic Range ◽  
Video Quality ◽  
Deep Convolutional Neural Network ◽  
Frame Rate

With the rapid advancement in many multimedia applications, such as video gaming, computer vision applications, and video streaming and surveillance, video quality remains an open challenge. Despite the existence of the standardized video quality as well as high definition (HD) and ultrahigh definition (UHD), enhancing the quality for the video compression standard will improve the video streaming resolution and satisfy end user’s quality of service (QoS). Versatile video coding (VVC) is the latest video coding standard that achieves significant coding efficiency. VVC will help spread high-quality video services and emerging applications, such as high dynamic range (HDR), high frame rate (HFR), and omnidirectional 360-degree multimedia compared to its predecessor high efficiency video coding (HEVC). Given its valuable results, the emerging field of deep learning is attracting the attention of scientists and prompts them to solve many contributions. In this study, we investigate the deep learning efficiency to the new VVC standard in order to improve video quality. However, in this work, we propose a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Thus, the VVC conventional in-loop filtering will be replaced by the suggested WSE-DCNN technique that is expected to eliminate the compression artifacts in order to improve visual quality. Numerical results demonstrate the efficacy of the proposed model achieving approximately − 2.85 % , − 8.89 % , and − 10.05 % BD-rate reduction of the luma (Y) and both chroma (U, V) components, respectively, under random access profile.

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