No-reference image quality assessment using shearlet transform and stacked autoencoders
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Symposium on Circuits and Systems |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1594-1597 |
| Volume | 2015-July |
| ISBN (Print) | 9781479983919 |
| Publication status | Published - 27 Jul 2015 |
Publication series
| Name | |
|---|---|
| Volume | 2015-July |
| ISSN (Print) | 0271-4310 |
Conference
| Title | 2015 IEEE International Symposium on Circuits and Systems (ISCAS 2015) |
|---|---|
| Location | Cultural Centre of Belém |
| Place | Portugal |
| City | Lisbon |
| Period | 24 - 27 May 2015 |
Link(s)
Abstract
In this work, we describe an efficient generalpurpose no-reference (NR) image quality assessment (IQA) algorithm that is based on a new multiscale directional transform (shearlet transform) with a strong ability to localize distributed discontinuities. The algorithm relies on utilizing the sum of subband coefficient amplitudes (SSCA) as primary features to describe the behavior of natural images and distorted images. Then, stacked autoencoders are applied to exaggerate the discriminative parts of the primary features. Finally, by translating the NR-IQA problem into classification problem, the differences of evolved features are identified by softmax classifier. The resulting algorithm, which we name SESANIA (ShEarlet and Stacked Autoencoders based No-reference Image quality Assessment), is tested on several databases (LIVE, Multiply Distorted LIVE and TID2008) and shown to be suitable to many common distortions, consistent with subjective assessment and comparable to full-reference IQA methods and state-of-the-art general purpose NR-IQA algorithms.
Research Area(s)
- No-reference image quality assessment, Shearlet transform, Softmax classification, Stacked autoencoders
Citation Format(s)
No-reference image quality assessment using shearlet transform and stacked autoencoders. / Li, Yuming; Po, Lai-Man; Xu, Xuyuan et al.
Proceedings - IEEE International Symposium on Circuits and Systems. Vol. 2015-July Institute of Electrical and Electronics Engineers Inc., 2015. p. 1594-1597 7168953.
Proceedings - IEEE International Symposium on Circuits and Systems. Vol. 2015-July Institute of Electrical and Electronics Engineers Inc., 2015. p. 1594-1597 7168953.
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review
