In this paper, we propose a convolutional neural network (CNN)-based no-reference image quality assessment (NR-IQA). Though deep learning has yielded superior performance in a number of computer vision studies, applying the deep CNN to the NR-IQA framework is not straightforward, since we face a few critical problems: 1) lack of training data; 2) absence of local ground truth targets. To alleviate these problems, we employ the full-reference image quality assessment (FR-IQA) metrics as intermediate training targets of the CNN. In addition, we incorporate the pooling stage in the training stage, so that the whole parameters of the model can be optimized in an end-to-end framework. The proposed model, named as a blind image evaluator based on a convolutional neural network (BIECON), achieves state-of-the-art prediction accuracy that is comparable with that of FR-IQA methods.
|Title of host publication||2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2018 Feb 20|
|Event||24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China|
Duration: 2017 Sept 17 → 2017 Sept 20
|Name||Proceedings - International Conference on Image Processing, ICIP|
|Other||24th IEEE International Conference on Image Processing, ICIP 2017|
|Period||17/9/17 → 17/9/20|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1A2B2014525).
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing