Name
Title | ||
|---|---|---|
Deeplearning Convolutional Neural Network Based Qoe Assessment Module For 4k Uhd Video Streaming |
Abstract | ||
|---|---|---|
With the rapid development of modern high resolution video streaming services, providing high Quality of Experience (QoE) has become a crucial service for any media streaming platforms. Most often it is necessary of provide the QoE with NR-IQA, which is a daunting task for any present network system for it's huge computational overloads and often inaccurate results. So in this research paper a new type of this NR-IQA was proposed that resolves these issues. In this work we have described a deep-learning based Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. This model processes the RAW RGB pixel images as input, the CNN works in the spatial domain without using any hand-crafted or derived features that are employed by most previous methods. The proposed CNN is utilized to classify all images in a MOS category. This approach achieves state of the art performance on the KoniQ-10k dataset and shows excellent generalization ability in classifying proper images into proper category. Detailed processing on images with data augmentation revealed the high quality estimation and classifying ability of our CNN, which is a novel system by far in these field. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.5220/0008117903920397 | SIMULTECH: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON SIMULATION AND MODELING METHODOLOGIES, TECHNOLOGIES AND APPLICATIONS, 2019 |
Keywords | Field | DocType |
Deep Learning, Convolutional Neural Network, Computer Networks, Video Steaming, 4K UHD, QoE | Computer vision,Convolutional neural network,Computer science,Reference image,Video streaming,Image quality,Real-time computing,Pixel,Quality of experience,RGB color model,Artificial intelligence,Deep learning | Conference |
Volume | Citations | PageRank |
2 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akm Ashiquzzaman | 1 | 1 | 0.69 |
| Sung Min Oh | 2 | 0 | 0.34 |
| Dongsu Lee | 3 | 1 | 0.69 |
| Hoehyeong Jung | 4 | 0 | 0.34 |
| Um Tai-won | 5 | 61 | 9.63 |
| Jinsul Kim | 6 | 81 | 25.54 |