Name
Abstract | ||
|---|---|---|
In this paper, we propose a strategy to optimize feature pooling and prediction models of video quality assessment (VQA) algorithms with a much smaller number of parameters than methods based on machine learning, such as neural networks. Based on optimization, the proposed mapping strategy is composed of a global linear model for pooling extracted features, a simple linear model for local alignment in which local factors depend on source videos, and a non-linear model for quality calibration. Also, a reduced-reference VQA algorithm is proposed to predict the local factors from the source video. In the IRCCyN/IVC video database of content influence and the LIVE mobile video database, the performance of VQA algorithms is improved significantly by local alignment. The proposed mapping strategy with prediction of local factors outperforms one no-reference VQA metric and is comparable to one full-reference VQA metric. Thus predicting the local factors in local alignment based on video content will be a promising new approach for VQA. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025108 | ICIP |
Keywords | Field | DocType |
optimisation,video signal processing,content influence,video quality assessment,no-reference vqa metric,neural networks,quality calibration,irccyn-ivc video database,global linear model,learning (artificial intelligence),video content,reduced-reference vqa algorithm,non-linear mapping,live mobile video database,feature extraction,video quality assessment algorithms,reduce reference,feature pooling,full-reference vqa metric,local factors,prediction models,machine learning,local alignment,source videos | Data mining,Computer science,Artificial intelligence,Predictive modelling,Artificial neural network,Video quality,Computer vision,Linear model,Pooling,Algorithm,Smith–Waterman algorithm,Calibration,Machine learning | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kongfeng Zhu | 1 | 22 | 1.36 |
| Marcus Barkowsky | 2 | 355 | 30.75 |
| Minmin Shen | 3 | 1 | 1.03 |
| Patrick Le Callet | 4 | 1252 | 111.66 |
| Dietmar Saupe | 5 | 1104 | 85.80 |