TY  - JOUR
ID  - SisLab4197
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4197/
A1  - Tung, Pham Thanh
A1  - Xiem, Van Hoang
A1  - Nghia, Nguyen Trung
A1  - Duong, Dinh Trieu
A1  - Ha, Le Thanh
Y1  - 2020/11//
N2  - In this paper, we present an experimental image quality assessment (IQA) method for image/ video patches with compression artifacts. Using the High Efficiency Video Coding (HEVC) standard, we create a new database of image patches with compression artifacts. Then, we conduct a completed subjective testing process to obtain the ?ground truth? quality scores for the mentioned database. Finally, we employ an end-to-end learning method to estimate the IQA model for the patches with HEVC compression artifacts. In such proposed method, a modified convolutional neural network (CNN) architecture is exploited for feature extraction while an adaptive moment estimation optimizer solution is used to perform the training process. Experimental results show that the proposed end-to-end IQA method significantly outperforms the relevant IQA benchmarks, especially when the compression artifacts are strongly realized.
PB  - IEEE
JF  - IEEE Access
SN  - 2169-3536
TI  - End-to-end Image Patch Quality Assessment for Image/Video with Compression Artifacts
AV  - public
ER  -