Paper Info
Title
Neural network solution fora real-time no-reference video quality assessment of H.264/AVC video bitstreams
Abstract
The ever-growing video streaming services require accurate quality assessment with often no reference to the original media. One primary challenge in developing no-reference (NR) video quality metrics is achieving real-timeliness while retaining the accuracy. A real-time no-reference video quality assessment (VQA) method is proposed for videos encoded by H.264/AVC codec. Temporal and spatial features are extracted from the encoded bit-stream and pixel values to train and validate a fully connected neural network. The hand-crafted features and network dynamics are designed in a manner to ensure a high correlation with human judgment of quality as well as minimizing the computational complexities. Proof-of-concept experiments are conducted via comparison with: 1) video sequences rated by a full-reference quality metric, and 2) H.264-encoded sequences from the LIVE video dataset which are subjectively evaluated through differential mean opinion scores (DMOS). The performance of the proposed method is verified by correlation measurements with the aforementioned objective and subjective scores. The framework achieves real-time execution while outperforming state-of-art full-reference and no-reference video quality assessment methods.
Year
DOI
Venue
2022
10.1007/s11042-021-10654-0
MULTIMEDIA TOOLS AND APPLICATIONS
Keywords
DocType
Volume
Neural network, No-reference, Video quality assessment, VQA, H.264/AVC
Journal
81
Issue
ISSN
Citations 
2
1380-7501
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Yasamin Fazliani100.34
Ernesto Andrade200.34
Shahram Shirani3141.36